Token Counts, Context Windows and Pricing by Model

Token Counts, Context Windows and Pricing by Model

The same prompt never costs the same number of tokens on two providers. Each model family ships its own tokenizer with its own vocabulary, so a paragraph that splits into 250 tokens for one model can split into 280 for another, and the price per million tokens, the context window and the output ceiling all change on top of that. This page collects those numbers for twelve models, one section each, grouped by provider: Anthropic's Claude Fable 5, Opus 5, Sonnet 5 and Haiku 4.5, OpenAI's GPT-5.6 Sol, Google's Gemini 3.1 Pro and 3.5 Flash-Lite, xAI's Grok 4, DeepSeek V4 Pro, Alibaba's Qwen 3.5 Plus, Moonshot's Kimi K3 and MiniMax M3.

How exact a count can be depends on what the provider publishes. In the Token Counter, the GPT-5.6 Sol row runs OpenAI's own o200k_base vocabulary. Without a browser-ready tokenizer from their makers, the DeepSeek and Qwen rows borrow cl100k_base and show a ~, and the Gemini, Grok, Kimi and MiniMax rows divide your character count by 3.8. Each section below says which method its row uses, how far the estimate can drift for your kind of text and where to get the provider's own count before a large batch.

Paste the same prompt into the counter once and read every row side by side. The context bar turns amber at 75 percent of a model's window and red at 95 percent, which is the point where the response has too little room left.

Claude Fable 5 Token Counter

At $10 per million input tokens, Claude Fable 5 is Anthropic's most expensive API model.

Fable 5 is the first publicly available model from Anthropic's Mythos class, a tier introduced above Opus for tasks that demand the highest level of reasoning and sustained coherence Anthropic currently offers. This tool counts it with Anthropic's published tokenizer package, which Anthropic describes as only a rough approximation for Claude 3 and later models, so treat the count as an estimate.⁶ Fable 5 supports a 1M-token context window at $10.00 per million input tokens and $50.00 per million output tokens, with a maximum output length of 128,000 tokens per response.¹

A 1M context window and a 128K output ceiling

Fable 5 processes up to 1 million tokens per request, matching the context ceiling of Claude Opus 4.8 and Sonnet 4.6. What sets Fable 5 apart at the context level is its output capacity: up to 128,000 tokens per response. Few competing models reach this ceiling. For tasks that require very long generated documents, detailed step-by-step technical analyses, or extended code outputs in a single call, the 128K output limit is a practical differentiator rather than a theoretical maximum.

When the context bar in this tool turns amber at 75% (750,000 tokens), you have 250,000 tokens of headroom remaining for the model's response and any additional dynamic content. For workloads that may produce responses approaching the 128K output ceiling, treat 700,000 input tokens as your operational threshold. The remaining 300,000 tokens of context accommodates the full output range without risking a context overflow error during response generation.

$10 input and $50 output per million tokens

Input tokens cost $10.00 per million and output tokens cost $50.00 per million, a five-to-one output-to-input ratio identical to Opus 4.8. Every token costs more on Fable 5; none count differently. For the same prompt at the same length, the INPUT $ column in this tool shows precisely twice the Opus 4.8 figure, because Fable 5 is exactly 2x the price of Opus 4.8 on both input and output.

For workloads that generate long outputs, the output premium grows quickly. A 10,000-token response at $50/M costs $0.50 in output alone; the equivalent response on Opus 4.8 costs $0.25. On tasks that routinely produce extended responses such as large code files, technical reports, or multi-chapter analyses, the two-to-one cost difference between models compounds significantly at production volume.²

How Fable 5 prompt caching reduces effective input cost

Fable 5 supports prompt caching at $1.00 per million tokens for cache hits, a 90% reduction from the standard $10/M input rate. For workflows where a large system prompt, reference document, or research context block is reused across many requests, caching dramatically reduces the per-request cost of that repeated content. At $1/M for cache hits, a 100,000-token cached context costs $0.10 per request rather than $1.00. Across 1,000 requests that reuse the same context block, caching saves $900 on that one component alone.³

How the Claude tokenizer counts Fable 5 prompts

The count is an estimate. This tool runs Anthropic's published @anthropic-ai/tokenizer library in your browser, and Anthropic says that package is no longer accurate for Claude 3 and later models and works only as a rough approximation.⁶ Anthropic's token counting endpoint returns the input token count for the exact model you name before you send a request, and the usage figures in each API response show what you were billed.⁷ Use the browser count to size a prompt quickly, then confirm billing-critical prompts with the endpoint.

For English prose, plan on approximately 250 tokens per 200 words. Code tokenizes less efficiently than prose: a Python function with type annotations and docstrings typically produces 30 to 50 percent more tokens than prose at an equivalent word count, and dense JSON with nested structures pushes the ratio even higher once indentation and bracket nesting compound. At Fable 5's $10/M input rate, a 20% estimation error on a 100,000-token prompt represents a $0.20 per-request cost variance, and across a production workload processing thousands of requests per day, that variance compounds into a material budget line item that exceeds the cost of implementing exact counting.

For production applications, call Anthropic's token counting endpoint from your request handler before each API call, passing the same model, system prompt and tools you plan to send.⁷ Its count comes from the model itself, giving you accurate cost projections and a reliable context window gate. Wrapping this check in a middleware function that logs the count and rejects requests exceeding your threshold keeps the validation logic centralized and prevents any single service from accidentally shipping an unbounded prompt to the API.

Planning inputs for the 128K output ceiling

At 128,000 output tokens, Fable 5 can deliver the equivalent of a full book chapter, a complete codebase migration plan, or a detailed multi-stage analysis in a single API response. Exploiting this capacity requires that your input leaves enough context headroom for the response. This constraint is stricter with Fable 5 than with models whose output caps at 4,000 or 8,000 tokens.

The ceiling is a capability, not a cost. Output cost on Fable 5 depends on actual response length, not on the maximum allowed. Explicit length constraints in your prompt reduce output token count and therefore output cost, even on a model capable of far longer responses. When your task does not need a 50,000-token output, instruct the model to respond concisely and pay only for the tokens generated.

Setting input token ceilings when output may reach 128K

For workflows that target long Fable 5 outputs, your input budget requires a tighter ceiling than for models with shorter output limits. If a response may reach 80,000 tokens, your input must leave at least 80,000 tokens of context window available after assembly. Set your application's pre-call validation threshold at 700,000 input tokens when targeting extended responses. In this tool, the 70% mark on Fable 5's context bar corresponds to 700,000 input tokens and is a practical visual checkpoint for long-form generation workloads.

When Fable 5 justifies the cost over Opus 4.8

Paste your typical system prompt and a representative user message into this Fable 5 versus Opus 4.8 cost readout, where the Fable 5 INPUT $ figure lands at exactly double the Opus 4.8 figure, the fixed cost of choosing Fable 5 over its cheaper sibling. That difference is consistent across every prompt regardless of content type or length, because the two models use identical tokenization.

For workloads where Opus 4.8 meets your quality bar consistently, such as document summarization, structured extraction, or standard code review, routing to Opus saves 50% on every request without changing anything else in your pipeline. The same prompt structure, the same context layout, the same API call format: only the model identifier and the price change.

Reserve Fable 5 for tasks where evaluation testing reveals a quality gap worth paying for: long-horizon agentic workflows spanning dozens of steps, complex technical migrations across large codebases, deep reasoning over contradictory evidence, or generation workloads that benefit from the 128K output ceiling. For production applications handling heterogeneous task complexity, a routing layer that escalates to Fable 5 only for genuinely hard requests captures the quality benefit at a blended cost well below the full Fable 5 rate.

US government export controls and API access

On June 12, 2026, the US Commerce Department issued an export control directive ordering Anthropic to suspend all access to Fable 5 and Mythos 5 for any foreign national. Because the scope extends to foreign nationals physically inside the United States, including Anthropic's own non-citizen employees, the company disabled Fable 5 and Mythos 5 for all users worldwide to ensure compliance. Access to Claude Opus 4.8, Sonnet 4.6, Haiku 4.5, and all other Claude models was not affected.⁴

Who the directive affects

The restriction applies to any foreign national, a scope broader than geography alone. US-resident developers without US citizenship fall within the directive's definition, as do non-citizen employees at Anthropic enterprise customers. The government cited a reported jailbreak technique that could unlock Mythos's cybersecurity capabilities through Fable 5's safeguards as the basis for the order. Anthropic disputed the severity of the finding, stating that it had reviewed the specific technique and that comparable capabilities were demonstrably available from other publicly deployed models not subject to similar controls.⁵

Which Claude models remain available and what to use in the meantime

For developers and organizations that had integrated Fable 5, the nearest unrestricted alternative on the Anthropic platform is Claude Opus 4.8. It shares the same 1M-token context window and costs $5/M input and $25/M output, exactly half the Fable 5 rate. The model string changes from claude-fable-5 to claude-opus-4-8; no other integration changes are required. Anthropic has stated it is working to restore access and believes the directive reflects a misunderstanding, but has not provided a specific timeline for restoration.

Because this tool counts both models with the same library and both share a 1M-token context window, the context-bar percentages it displays for Fable 5 apply unchanged to Opus 4.8, so you can reuse the same token-budget guardrails without recalculation. Cost projections for Opus 4.8 are simply half the Fable 5 figures, which makes it straightforward to model the savings from routing a workload to the cheaper model before the access directive is lifted.

When to use this

Use this when building prompts for Fable 5 and you need to verify your token count fits within the 1M context window with adequate headroom for extended responses. Paste your prompt and check the Fable 5 row directly; the same view shows Opus 4.8 right beside it so you can confirm the two-to-one cost gap on your own text. You should also use it to compare Fable 5 and Opus 4.8 input costs for the same prompt before committing to a model choice for a complex task.

Examples

System instructions: 2,000 tokens. Repository context (Python, 3,000 files): 500,000 tokens. Migration task description: 1,500 tokens. Total: ~503,500 tokens.

Context usage: ~50% of the 1M window. Input cost: ~$5.04 at $10/M. Compare to Opus 4.8 for the same prompt: ~$2.52 at $5/M.

System prompt: 1,200 tokens. 15 research papers (average 10,000 tokens each): 150,000 tokens. Synthesis instructions: 800 tokens. Total: ~152,000 tokens.

Input cost: ~$1.52. Output (comprehensive analysis, 30,000 tokens): ~$1.50 in output cost. Total: ~$3.02 per request.

Claude Opus 5 Token Counter

A premium Claude model for research, coding, and long-context reasoning, Opus 5 is Anthropic's flagship Opus-tier option, priced identically to its predecessor Opus 4.8.¹²

It supports a 1M-token context window at $5.00 per million input tokens and $25.00 per million output tokens. Opus 5 carries forward the tokenizer introduced with Opus 4.7, not the older @anthropic-ai/tokenizer package.³ For production estimates, Anthropic recommends model-specific token counting through the Messages API, and the API count can differ slightly from billed input tokens.⁴

1M tokens on the Claude API, Bedrock and Vertex AI

Claude Opus 5 processes up to 1 million tokens per request on the Claude API, Amazon Bedrock, and Vertex AI.¹ That capacity fits the equivalent of several long novels, an entire codebase, or hours of transcribed meetings. Reaching the limit is possible for advanced research or analysis workflows that chain many retrieved documents.

When the bar in this tool turns amber at 75%, you have roughly 250,000 tokens of headroom remaining: enough for a substantial response but a signal to review whether all included context is necessary. For most research workflows, the practical ceiling is closer to 700,000 tokens to leave adequate room for long analytical outputs.

Planning Opus 5 prompts before long-context retrieval

If your workflow assembles many retrieved chunks, count the system prompt, retrieval set, and user question together before adding the final instruction. That pre-check shows whether the prompt needs trimming or chunk splitting before the API call. For research pipelines that pull from a vector database, the retrieval step often contributes more tokens than the system prompt and user query combined, so measuring the full assembled context after retrieval rather than before gives you the accurate count that determines whether the request will succeed.

$5 input and $25 output per million tokens

Opus 5 input tokens cost $5.00 per million and output tokens cost $25.00 per million, unchanged from Opus 4.8.⁴ This five-to-one ratio means long generated outputs, such as detailed analyses, multi-step code, or lengthy reports, dominate the bill, so the format you choose for the response has a direct and measurable impact on every request's cost.

A 2,000-token response at $25/M costs five cents, while the same input prompt at $5/M costs only one cent for a 2,000-token prompt. Structured extraction tasks that return compact JSON are far cheaper per request than open-ended generation, which is why designing output format constraints early in your prompt engineering process reduces costs before you ever hit production scale.

Choosing output formats before scaling

For batch jobs, compare open-ended prose, bullet lists, and compact JSON on representative prompts before you scale the workflow. A single long-form prose response might consume 2,000 output tokens, while the same information structured as a compact JSON object uses fewer than 200 tokens; at Opus 5 output pricing of $25 per million tokens, that difference of 1,800 tokens saves $0.045 per request, which compounds to $45,000 per day at a volume of 1 million requests.

Why the published Claude tokenizer is only an approximation

Claude Opus 5 carries forward the tokenizer introduced with Claude Opus 4.7, and the published @anthropic-ai/tokenizer package is only a rough approximation for Claude 3 and later models.³ For production estimates, use Anthropic's model-specific token counting endpoint and pass the same model you plan to call, because the endpoint accounts for model-specific vocabulary merges that the generic package cannot reproduce.

For English prose, expect roughly 250 tokens per 200 words, though the exact ratio varies by vocabulary size and content type.⁵ For code, token density depends on language and formatting conventions: Python with type annotations and docstrings runs higher than minimal shell scripts, and dense JSON with nested structures pushes even higher. Always count tokens for code-heavy prompts rather than estimating from line count, since a 50-line Python file can easily produce 800 or more tokens once annotations, docstrings, and operators are fully tokenized.

Running the Anthropic tokenizer directly in your application

Running Anthropic's token-counting endpoint in your application gives you model-specific counts before each API call to Opus 5, with no estimation involved. The endpoint accepts the same structured inputs as a Message request, including system prompts, tools, images, and PDFs, and returns the total input token count that matches what the API will bill.

Building a pre-call validation function into your pipeline prevents context overflow errors from reaching the API, which is far cheaper than handling error responses after the fact. For Opus 5, set your pre-call limit at 800,000 tokens to leave 200,000 tokens of headroom for generated output, a threshold that accommodates responses up to 200,000 tokens and covers the most detailed analyses and extended code outputs Opus typically generates.

Choosing between Opus 5 and Sonnet 5 for your workload

Both Opus 5 and Sonnet 5 share a 1M-token context window and the same tokenizer, so the same prompt fits both models without any modification to its structure or content. The decision reduces to quality requirements and budget, since the per-request cost difference is the main variable that changes when you switch between them. For complex multi-step reasoning, nuanced code refactoring, and research synthesis across many documents, Opus 5's additional capability can justify the 150% input price premium over Sonnet 5, but only if your evaluation testing confirms a measurable quality gap on your specific task type.

When to route between Opus and Sonnet

For production applications, a routing layer that scores prompt complexity and sends simple requests to Sonnet and complex requests to Opus balances cost and quality automatically. A task classifier running on Haiku (at $1/M input) can make this routing decision for a fraction of what Sonnet or Opus charges per request. Count the tokens for both your complexity-scoring prompt and your primary task prompt using this tool before designing the routing logic, so your cost model accounts for the overhead of the classifier itself.

Building the classifier prompt itself in this tool keeps its cost visible, since a routing layer that fires on every request adds a small but nonzero token overhead that compounds across millions of calls in a high-volume system. Measuring that overhead alongside the savings from routing simple work to Sonnet is what turns a theoretical 60 percent discount into a realized one on your monthly invoice.

When to use this

Use this before sending long-context research or analysis prompts to Opus 5, where you can watch the Opus 5 context bar hit amber at the 75% mark before you ever send the request. You should verify token counts when your prompt exceeds 50,000 tokens to ensure your context fits and to confirm your per-request cost before running a large batch.

Examples

System instructions: 1,200 tokens. Research paper (30 pages): 22,000 tokens. Analysis prompt: 300 tokens.

Total: ~23,500 tokens. Input cost: ~$0.12 per request at $5/M.

Three policy documents, 8,000 tokens each, plus a comparison prompt of 400 tokens. Total: ~24,400 tokens.

Input cost: ~$0.12. Detailed comparison response at 3,000 tokens: ~$0.075 in output cost.

Claude Sonnet 5 Token Counter

For everyday production work, Sonnet 5 balances strong reasoning, a 1M-token context window, and lower pricing than Opus.¹

It uses the same tokenizer Anthropic introduced with Opus 4.7 and that Opus 5 carries forward, while Anthropic's legacy tokenizer package is only a rough approximation for Claude 3 and later models.² For production estimates, use the model-specific token-counting endpoint; Anthropic says its count is an estimate that can differ slightly from the input tokens used when creating a message.³ Sonnet 5 costs $2.00 per million input tokens and $10.00 per million output tokens, introductory pricing that runs through the end of August 2026.⁴

A 1M-token context for long RAG prompts

Sonnet 5 supports a 1M-token context window on the Claude API, Amazon Bedrock, and Vertex AI.¹ That capacity lets you keep long documents, extended conversation histories, and detailed system instructions in one request when your assembled prompt still leaves room for output, which means most production RAG pipelines can pass entire research papers or multi-page contracts directly to the model without any pre-processing overhead.

The amber warning at 75% means roughly 250,000 tokens remain before the 1M-token ceiling, which is enough room for a substantial response but also a signal that the prompt is approaching the zone where overflow becomes a real risk. For production pipelines, treat that threshold as a planning checkpoint: if your assembled prompt is already near 750,000 tokens, you need to decide whether to trim context, split retrieval chunks, or reserve more output budget before the API call reaches the model. Automating this checkpoint in your request assembly code eliminates the need for manual review of every prompt length.

Using Sonnet 5 for repeatable production prompts

For support bots, code review tools, and internal knowledge assistants, count a representative prompt before you set retry and chunking rules. A stable token baseline makes it easier to spot when new instructions, retrieved documents, or conversation history push the request toward the warning zone. Once you have that baseline, document it alongside the expected output length so that every future prompt engineering change can be measured against the original token budget; this practice prevents the gradual token creep that silently increases per-request costs and reduces available context headroom over weeks of iterative prompt adjustments.

$2 input and $10 output at the introductory rate

Sonnet 5 input tokens cost $2.00 per million and output tokens cost $10.00 per million at the current introductory rate.⁴ Compared with Opus 5 at $5/M input and $25/M output, Sonnet is 60% cheaper in both directions while keeping the same five-to-one output-to-input price ratio, which means the cost savings are proportional regardless of whether your workload is input-heavy or output-heavy.

How output cost shapes the Sonnet-versus-Opus decision

For high-volume applications where most tasks do not require frontier reasoning, that 60% input discount compounds quickly. The breakeven point depends on how much of your workload needs Opus-level reasoning, so a tiered routing approach often works better than choosing one model for every request. When output length is factored in, the absolute dollar gap widens fast: Opus 5 charges $25 per million output tokens compared to Sonnet 5 at $10 per million, so workloads generating long responses see the same 60% discount translate into a much larger dollar figure per request.

The Opus 4.7 tokenizer and migrating from Sonnet 4.6

Sonnet 5 uses the same tokenizer Anthropic introduced with Opus 4.7, the tokenizer Opus 5 also carries forward, rather than an older tokenizer generation specific to Sonnet.² Migrating from Sonnet 4.6, expect the same prompt to tokenize to more tokens than it did before, since Sonnet 4.6 ran on an earlier tokenizer. Anthropic puts that increase at roughly 1.0 to 1.35 times, depending on the content type.⁵ That shift changes cost projections for any prompt you had already baselined, not because the new tokenizer is less accurate, but because the count itself moved.

For production billing verification, call the token-counting endpoint with the same structured message you plan to send. It accepts system prompts, tools, images, and PDFs, then returns an input token estimate for the selected model.³ Use that estimate for routing and budgeting, and leave a small buffer because actual message creation can differ slightly.

Verifying billing-critical prompts before launch

For prompts near a budget or context limit, compare the raw-text count with the target-model endpoint before you ship the integration. Run at least 20 representative requests through the actual Sonnet 5 endpoint and compare the billed token counts from the usage metadata against your preflight estimates; any consistent discrepancy above 5% means your estimation method needs recalibration before you rely on it for production cost projections or context window enforcement.

Treat the 5 percent threshold as a planning guardrail rather than a hard pass, because a single outlier prompt can mask a systematic drift that only shows up once you aggregate usage across a full day of traffic. Repeating the comparison on a fresh batch of representative requests each time you change the system prompt or tools keeps the billing estimate honest as the integration evolves.

Sonnet 5 as the production default for high-volume API applications

Sonnet 5 fills the gap between cost and capability for most production workloads, offering strong reasoning quality at a price point that scales predictably. At $2/M input and $10/M output, it gives teams a lower-cost default for chat interfaces, document analysis tools, and code review assistants while preserving a path to Opus for requests that need deeper reasoning.

For engineering teams making an initial model choice, paste your system prompt and a representative user message into this tool and compare the INPUT $ column between Sonnet 5 and Opus 5. The cost difference for a single request is small; the compounding effect over millions of daily requests makes the choice consequential. Even a $0.002 per-request saving at 1 million daily requests amounts to $2,000 per day, or $730,000 per year.

Prompt portability between Sonnet 5 and Opus 5

For A/B testing between Sonnet and Opus, run the same set of prompts through both models and compare output quality against cost using a structured evaluation rubric. Keep your token-counting method consistent, then compare the resulting input and output costs against the price table to quantify the per-request savings at your actual prompt lengths. For tasks where you observe no quality difference in your evaluation set, Sonnet 5 is the better production choice, and documenting your evaluation criteria and results ensures the model choice can be revisited if task requirements change.

The portability between these two models is a direct consequence of their shared tokenizer and context window: swapping from Sonnet to Opus requires changing only the model string in your API call, with no prompt restructuring, no context re-chunking, and no token budget recalculation. That portability holds between Sonnet 5 and Opus 5 specifically; a token count taken on Sonnet 4.6 does not carry over to Sonnet 5 without re-baselining, since the tokenizer itself changed between those two versions. Because Sonnet 5 and Opus 5 share a tokenizer, matching Sonnet 5 and Opus 5 token counts takes a single paste rather than a separate recount, which is what makes it practical to start every new workload on Sonnet and switch to Opus only for the task categories where the quality difference justifies the premium.

When to use this

Use this when building production pipelines on Claude Sonnet 5 where you need to verify prompt costs at scale. You should also use it to compare Sonnet versus Opus costs for the same prompts before committing to a model choice for a new feature.

Examples

System prompt: 900 tokens. Conversation history (5 turns): 1,800 tokens. User message: 120 tokens. Total: ~2,820 tokens.

Input cost: ~$0.0056 at $2/M input. At 50,000 requests/day: ~$282/day in input costs.

A 500-line Python file: ~3,000 tokens. Code review instruction prompt: 400 tokens. Total: ~3,400 tokens.

Input cost: ~$0.0068 per file. Output (detailed review, ~800 tokens): ~$0.0080. Total: ~$0.015 per review.

Claude Haiku 4.5 Token Counter

At high request volume, Haiku 4.5 gives Claude users a fast, inexpensive tier for focused work.¹

It gives teams a practical first tier for chat, extraction, routing, and coding sub-agents, priced at $1.00 per million input tokens and $5.00 per million output tokens on the Claude API pricing table.² Its 200K-token context window is large enough for many documents and conversations, but it is still a hard ceiling that you should check before sending a prompt. The legacy tokenizer package is no longer exact for Claude 3 and later models, so treat raw-text counts as estimates and confirm billing-critical prompts with Anthropic's target-model endpoint.³

A 200K window and how much headroom to keep

Haiku 4.5 has a 200,000-token context window on the Claude API, Amazon Bedrock, and Vertex AI.⁴ Treat that ceiling as the maximum amount of text the model can see and generate against, not as a target you should fill. When the context bar reaches amber at 75%, you have about 50,000 tokens of headroom before the 200K limit, so use that threshold to trim history or split long documents before the request reaches the API.

For large codebases, long policy documents, or many accumulated conversation turns, the planning question is simple: can the assembled prompt fit under 200K while still leaving enough room for a useful response? If the answer is no, chunk the input, summarize older turns, or move the request to a model with a larger context window.

Keeping Haiku prompts focused under 200K

Haiku works best when each request has a narrow job and only the needed context. Count the full assembled prompt, then remove stale conversation turns or duplicate retrieved chunks before the model sees the request. A focused 5,000-token prompt that fits easily within Haiku 4.5's 200,000-token window leaves 195,000 tokens of headroom for the response, but a sprawling 150,000-token prompt assembled from multiple retrieved documents and a long conversation history leaves only 50,000 tokens for the model to generate its answer, which may not be sufficient for the detailed analysis the task requires.

$1 input and $5 output per million tokens

Haiku 4.5 input costs $1.00 per million tokens, and output costs $5.00 per million tokens.² That five-to-one output-to-input ratio matches the rest of the Claude model family and makes short, structured tasks particularly inexpensive, especially when the prompt is small and the response is a compact label, summary, or extraction rather than a long-form answer.

Calculating request costs at high volume

A 1,000-token prompt with a 200-token classification response costs about $0.001 per request in combined input and output at Haiku pricing, which means a pipeline processing 100,000 items per day spends roughly $100 total. The savings become meaningful when that pattern repeats across thousands of requests, but the model choice should still depend on quality: Haiku is the right default for simple tasks only when your evaluation set confirms that the output quality meets your workflow requirements. Running a structured evaluation on at least 100 representative items from your dataset before committing to a production model prevents the expensive discovery that a cheaper model produces unacceptable quality after the pipeline is already deployed.

Sizing Haiku 4.5 prompts without an exact tokenizer

The legacy @anthropic-ai/tokenizer package can be useful for rough local estimates, but Anthropic says it is no longer accurate for Claude 3 and later models and should not be treated as exact.³ For Haiku 4.5, use the token count as a sizing estimate and confirm billing-critical prompts with the target model.

Anthropic provides a token-counting endpoint that accepts the same structured inputs as a message, including system prompts, tools, images, and PDFs, then returns the input token count for the selected model.⁵ The response is still an estimate, and actual message creation can differ slightly from the pre-call count, so leave a buffer of at least 5 percent when your prompt is close to a context or budget limit to absorb that variance.

Building a tiered routing pipeline with Haiku as the first tier

Building a tiered routing pipeline with Haiku 4.5 as the first tier is a common cost-control pattern for high-volume Claude applications that process a mix of simple and complex requests. Route simple classification, extraction, routing, and summarization requests to Haiku first, then escalate only the requests that fail a quality check or need deeper reasoning, rather than paying frontier-model prices for every single call.

For classification and extraction tasks, a quality threshold might check whether the JSON response is valid and whether the confidence score meets your minimum acceptable level. For generation tasks, a reviewer model, keyword scan, or output length check can flag weak responses that need escalation to a more capable model. When the check triggers escalation, include the Haiku response in the follow-up prompt so Sonnet or Opus can see the first attempt and build on it rather than starting the reasoning process from scratch.

Setting escalation thresholds for Haiku routing

For each workflow, define the smallest quality signal that justifies escalation before you send the first production request. A practical threshold might require that Haiku's response contains valid JSON with all required fields and a confidence score above 0.85; any response that fails either check gets escalated to Sonnet or Opus with the original Haiku output included as context. Defining these criteria before launch prevents ad hoc escalation decisions that undermine the cost savings the routing layer was designed to deliver.

Document the thresholds in the same repository as the routing code so the next engineer can see exactly why escalation triggers where it does, which prevents the silent drift toward over-escalation that erodes the savings over time. Reviewing the escalation rate monthly against actual quality outcomes lets you tighten or loosen the criteria as the downstream model lineup changes.

Haiku 4.5 context usage across common workload types

On the context spectrum across the Claude family, Haiku 4.5's 200K limit sits below the 1M-token windows available on newer Opus and Sonnet models.⁴ That makes it a strong fit for focused document analysis, chat histories, and structured extraction jobs, but not for prompts that need to hold a large codebase and many retrieved chunks in one request.

The 200K limit becomes a real architectural constraint when a workflow accumulates long transcripts, large policy documents, or many RAG chunks. Use this tool to check the full assembled prompt token count before designing around Haiku's context ceiling; the difference between a 150,000-token prompt and a 220,000-token prompt determines whether Haiku can handle the request directly or whether you need chunking.

When to use this

Use this to estimate costs for high-volume, low-complexity tasks like classification, entity extraction, short summarization, and routing. Run a classification prompt through the Haiku-Sonnet-Opus tier picker to see all three costs side by side before choosing where the request should go. You should also count Haiku tokens when building pipelines that process thousands of items per day, because the per-token cost advantage compounds only if the model quality is sufficient for the task.⁵

Examples

500 emails per hour. Average prompt: 800 tokens (email + instruction). Average response: 50 tokens (category label).

Input cost: $0.0004 per email. Daily cost at 12,000 emails: ~$4.80 in input. Compare to Sonnet: ~$14.40.

A 180,000-token document nearly fills Haiku's context window, leaving little room for analysis output.

Split into two chunks: 90,000 tokens each with overlap. Each request fits well under the 200K limit.

GPT-5.6 Sol Token Counter

GPT-5.6 Sol charges per token, not per character.

Every prompt you send runs through the o200k_base tokenizer, the same vocabulary OpenAI publishes openly so developers can count tokens before the API call arrives.¹ A system prompt, a few conversation turns, and a document together can reach thousands of tokens before the model writes its first word. Sol is the flagship of the GPT-5.6 lineup and offers a 1.05M-token context window at $5.00 per million input tokens and $30.00 per million output tokens, making output the dominant cost for most generative workloads.²

1.05M tokens, the largest window in the counter

GPT-5.6 Sol allows 1.05 million tokens per request, the largest context window among all models this tool covers, narrowly ahead of Kimi K3. Practically, that fits roughly 785,000 words of plain text, or the equivalent of several long novels loaded into a single request. Conversation history grows the token count with every turn: once the thread reaches the limit, the API rejects the request or silently truncates the oldest context, and neither outcome is desirable for a production application.

Consequently, developers who build chat applications must monitor accumulated token counts across turns, not just in the current message. This tool shows total context usage as a percentage, so overflow is visible before the request fires. Set your application's soft limit at 75% of the context window to leave room for the model's response without risking a context overflow error.

Tracking GPT-5.6 Sol context across conversation turns

For chat interfaces, store the running input token total after each assistant reply and compare it with the 1.05M limit before the next request. This catches slow growth from multi-turn history earlier than waiting for the API to reject the payload. A thread that accumulates 500 tokens per turn reaches the 75% warning threshold at 1,575 turns, but a thread with verbose assistant replies averaging 2,000 tokens per turn hits the same threshold in fewer than 394 turns. Tracking the running total after every response lets your application trigger a summarization pass or prune older history before the context window becomes a hard constraint.

$5 input and $30 output: the six-to-one ratio

Input tokens cost $5.00 per million and output tokens cost $30.00 per million, a six-to-one output-to-input pricing ratio that makes every generated token considerably more expensive than every token you send.² Building on this ratio: generating a 1,000-token response is six times more expensive than sending a 1,000-token prompt, which means output length is the single largest lever for controlling GPT-5.6 Sol spending in any application that produces reports, translations, or code files.

Controlling GPT-5.6 Sol output costs at scale

Batch workloads that send thousands of requests per day multiply these per-token costs linearly, so a 10% reduction in average response length can cut total spending significantly.³ Specifying structured output constraints with a JSON schema and adding an explicit length instruction is often the most direct lever for reducing GPT-5.6 Sol costs at scale.⁴ At $30 per million output tokens, every token of unnecessary prose in the response carries a measurable cost; a 500-token reduction per request across 50,000 daily requests saves $750 per day in output spending alone.

Exact counts with the o200k_base vocabulary

Because GPT-5.6 Sol uses o200k_base, an open-source vocabulary file, this tool produces an exact count rather than an estimate, so the number you see is the number OpenAI bills. Common English words typically map to a single token, but dense content such as URLs, JSON keys, inline code, and non-Latin characters splits into more tokens per character than prose, which is why symbol-heavy prompts always cost more than a word-count estimate suggests.

Counting tokens before the call avoids both context overflow and unexpected cost spikes for unusually long or symbol-heavy prompts. For prompts that include embedded JSON payloads or structured data, count them separately from prose sections to understand which part of your prompt carries the most token weight and where compression would have the greatest impact on per-request cost.

Checking dense prompts before API calls

For prompts with code, JSON, or non-Latin text, count the assembled payload before choosing a context or budget threshold. A prose-only estimate can undercount a mixed-content prompt by 30 to 50 percent, meaning a payload that appears to fit comfortably within the context window at 600,000 tokens might actually exceed 900,000 tokens once symbols, brackets, and non-Latin characters are properly tokenized. Running the full assembled prompt through a pre-flight count before selecting the request budget prevents both overflow errors and unexpected cost spikes from dense content that the prose estimate missed.

Running this pre-flight count as a standard step in your release process catches the gap before it reaches production, where a single overflow error can abort a batch job and waste the tokens already spent on earlier steps. Teams that measure the prose-versus-symbol gap on their own most common prompts can set a context threshold that reflects their real content mix rather than a generic rule.

Counting tokens programmatically with o200k_base

When your application needs to verify token counts before sending a request, the tiktoken library gives you direct access to o200k_base.⁵ In Python, install it with pip install tiktoken, then call enc = tiktoken.get_encoding('o200k_base'); count = len(enc.encode(text)). This produces the same count this browser tool produces, running locally in your application without any API call.

For Node.js applications, the tiktoken npm package exposes the same encoding. Import get_encoding from the package, call get_encoding('o200k_base'), and encode your text to get an integer array whose length is the token count. Adding a pre-flight check to your request handler that raises an error when the token count exceeds 90% of the context window prevents runtime errors in production and keeps per-request costs predictable.

Reducing output cost for high-volume GPT-5.6 Sol workloads

For high-volume workloads on GPT-5.6 Sol, output token cost is the primary lever because the six-to-one output premium makes long responses disproportionately expensive. A 2,000-token prose explanation costs $0.060 in output alone; the same information structured as a 400-token JSON response costs $0.012. Specifying the response_format parameter with a JSON schema and adding an explicit length instruction reduces output tokens without reducing information density.

Batch processing through the OpenAI Batch API reduces input cost by 50% for non-time-sensitive workloads, dropping the effective input rate to $2.50 per million. For pipelines where same-day response is not required, combining batch mode with a concise output format can reduce total GPT-5.6 Sol cost by 60–70% per request. Structured output alone does not guarantee savings without measurement: pasting the before-and-after prompt into the GPT-5.6 Sol batch-pricing view and reading the live INPUT $ figure is what proves a schema rewrite actually shrank the token count before you submit at the discounted Batch API rate.

When to use this

Use this before building any pipeline that calls GPT-5.6 Sol at scale. You should verify token counts whenever your prompt combines a system instruction, retrieved documents, and a conversation history, since accumulation across those sources surprises most developers.

Examples

System prompt: 600 tokens. Retrieved chunk: 3,200 tokens. User question: 40 tokens. Total: ~3,840 tokens.

Cost per request: ~$0.019 at $5/M input. At 10,000 requests/day: ~$192/day in input alone.

Average 300 tokens per turn. By turn 10, accumulated history reaches ~3,000 tokens plus each new message.

Summarize older turns once the thread passes 2,000 tokens to keep context usage predictable.

Gemini 3.1 Pro Token Counter

A 1,048,576-token input limit and 65,536-token output limit make Gemini 3.1 Pro Preview one of the widest-context options for document-heavy workflows.¹

Because this tool runs in the browser, it uses a character-division estimate rather than Google's model tokenizer. For Gemini text, Google describes one token as roughly four characters, so the estimate is best for sizing prompts before you send them.² Gemini 3.1 Pro Preview uses tiered pricing: $2.00 per million input tokens and $12.00 per million output tokens for prompts up to 200K tokens, and $4.00 per million input tokens and $18.00 per million output tokens for prompts above 200K tokens.³

1,048,576 input tokens and a 65,536-token output cap

Gemini 3.1 Pro Preview allows 1,048,576 input tokens per request, with a 65,536-token output limit.¹ That gives large-document analysis, research workflows, and retrieval pipelines plenty of room, but the limit still includes the response budget you reserve for the model, which means the output ceiling is a separate constraint you must account for when planning long-generation tasks.

The amber warning at 75% means about 262,144 tokens remain before the 1,048,576-token ceiling, providing substantial headroom for both retrieved context and model output. Use that threshold as a planning signal: trim retrieved context, summarize older conversation turns, or split the input before the request reaches the API, since exceeding the limit at runtime forces either an error or silent truncation that degrades output quality.

For research pipelines that load multiple documents, count each document's token contribution individually before assembling the full prompt. A workflow that retrieves five documents averaging 40,000 tokens each consumes 200,000 tokens on retrieval alone; combined with a system prompt and user query, the assembled request may already be near the 50% mark before the model generates a single output token. Breaking down the budget by component lets you identify which documents to truncate or summarize before the API call.

Tiered pricing below and above 200K-token prompts

Gemini 3.1 Pro Preview pricing is tiered by prompt size, which makes cost projection more nuanced than a flat-rate model but also creates an opportunity to optimize spending by keeping prompts under the tier boundary.³ For prompts up to 200K tokens, input costs $2.00 per million tokens and output costs $12.00 per million tokens. For prompts above 200K tokens, input costs $4.00 per million tokens and output costs $18.00 per million tokens, effectively doubling the input rate once the threshold is crossed.

Calculating request costs at high volume

A 5,000-token prompt with a 5,000-token response costs about $0.010 input plus $0.060 output, or $0.070 total, when the prompt stays under 200K tokens and qualifies for the lower tier. Sampling your prompt distribution before committing to a batch volume is the only reliable way to project costs when the tier boundary falls within your expected range, and measuring actual prompt lengths across your full dataset prevents the surprise of discovering that your average request crossed the threshold only after the first month of billing.

Why Gemini 3.1 Pro counts are marked ~

The ~ symbol on Gemini 3.1 Pro counts indicates that this is an approximation rather than an exact count derived from Google's proprietary model tokenizer. Google tokenizes text, images, audio, video, and other input modalities through the Gemini API using a vocabulary that is not published as a browser-compatible library, so the browser estimate here cannot reproduce that model-specific tokenization exactly and will carry some margin of error.²

Use these counts for budgeting, prompt sizing, and context-window checks, not for exact billing projection, since the character-division estimate cannot reproduce Google's model-specific tokenization of text, images, and other modalities. When precision matters, count the assembled request with the same model you plan to call, and treat the API-side count as the authoritative figure for any billing-critical cost projection.

Adding a buffer for batch cost projections

For batch planning, add a conservative buffer to the browser estimate before multiplying by request volume, since the buffer absorbs tokenizer variance while still showing whether the workload belongs under the 200K pricing tier or above it. A 20% buffer on a 150,000-token estimate brings the planning figure to 180,000 tokens, which keeps the request safely within the lower pricing tier even if the actual count runs higher than the browser approximation suggests.

Getting an exact Gemini 3.1 Pro count via the countTokens API

Getting a model-specific token count before sending to Gemini 3.1 Pro requires the Gemini API countTokens method, which runs the actual model tokenizer on your input content and returns the precise token count for the prompt.⁴ The API reference says models.countTokens processes the same content the model will see at inference time, so the count accounts for all modality-specific tokenization including text, images, and other input types that the browser estimate cannot reproduce. Google describes the same method as the way to prevent requests from exceeding the model context window and to estimate potential costs before the request is sent.⁵

Using countTokens for staged prompt assembly

For pipelines that manage context budgets programmatically, call countTokens after each assembly step: first after loading the system prompt, then again after appending retrieved chunks, and finally after adding the user message. If any intermediate count exceeds your target threshold, trim the retrieved context before reaching the final assembly. This staged counting approach prevents expensive API calls that would return a context overflow error and reveals exactly which step pushed the prompt over the limit, giving you a clear signal for where to apply compression or chunking. Running both the browser estimate and the API countTokens method on the same prompt also lets you measure the approximation error for your specific content type, which helps you set an appropriate buffer for pre-filtering inputs before the more expensive API-side count.

Build the assembled count into a lightweight pre-send check so the expensive API call never fires until the staged total clears your target window, which is especially valuable when many requests share a large retrieved context that changes between calls. Pairing the browser estimate with the staged API count gives you both a fast local signal and a precise server-side one without paying for the API count on every draft.

When Gemini 3.1 Pro pricing makes sense versus other 1M-context models

When your workload needs 1M-context capacity and produces long outputs relative to inputs, Gemini 3.1 Pro Preview's two-to-one input-to-output ratio can be easier to budget than models with steeper output pricing, especially when prompts stay under the 200K threshold. A request with 10,000 input tokens and 10,000 output tokens costs about $0.020 input plus $0.120 output, or $0.140 total, when the prompt qualifies for the lower pricing tier.

For workloads that generate verbose outputs, such as long analyses, detailed reports, or extended code files, the advantage grows as output length increases relative to input, because the two-to-one ratio scales more gently than the five-to-one or six-to-one ratios used by Claude and GPT. Compare your typical input-to-output ratio against the tiered price table before committing to a production model, then paste your prompt to see where it lands against the 200K Gemini 3.1 Pro tier boundary and whether it qualifies for the lower rate before you send it.

When to use this

Use this when sizing prompts for Gemini 3.1 Pro before sending. You should treat the count as an estimate for cost modeling and verify billing-critical requests with the Gemini API countTokens method.⁴

Examples

System prompt: 400 tokens. Document (50 pages): ~38,000 tokens. Questions: 200 tokens. Total estimate: ~38,600 tokens.

Input cost estimate: ~$0.077 at the short-prompt input rate. At 1,000 requests: ~$77 input cost.

Average article: 2,500 tokens. System prompt: 500 tokens. Average summary output: 300 tokens.

Total per request: ~3,000 tokens in, 300 out. Cost: ~$0.0096 at the short-prompt rates. At 10,000/day: ~$96/day total.

Gemini 3.5 Flash-Lite Token Counter

Flash-Lite is built for low-cost, high-throughput Gemini workloads where speed and price matter more than frontier reasoning depth.

At $0.30 per million input tokens and $2.50 per million output tokens, Flash-Lite carries an output-to-input ratio like most other models, but its absolute rate is still a fraction of what frontier models charge, which makes it much cheaper in real dollar terms even though the ratio itself is not flat. Flash-Lite uses Google's model-specific tokenizer, which is not available as a general browser library for every Gemini model, so token counts here use a character-division estimate accurate to 10-15% for typical English text.¹

A 1M window at a fraction of the Gemini 3.1 Pro price

Flash-Lite supports a 1,000,000-token context window, matching the capacity of Gemini 3.1 Pro at roughly one-seventh the price.² Run your own prompt through the Flash-Lite versus Gemini 3.1 Pro price gap check to see how much of that sevenfold difference applies to your actual content; for workloads that process large volumes of long documents, this combination of capacity and cost is difficult to match among currently available models.

Pipelines that previously had to truncate documents to fit cheaper models can now pass full context to Flash-Lite without the same cost penalty, removing a whole class of preprocessing logic from existing batch workflows. The amber threshold at 75% context usage is still worth monitoring in batch pipelines; approaching the 1M limit triggers the same overflow risk on Flash-Lite as on any model, despite the low per-token cost.

Watching Flash-Lite batch jobs before the 1M limit

For batch queues, count the assembled prompt before each request and flag anything above your chosen soft limit. Low per-token pricing does not remove the API's context ceiling, so the queue should split or trim prompts before they fail. A batch job processing 50,000 documents per day where each document averages 10,000 tokens means the queue processes 500 million tokens daily; even at Flash-Lite's $0.30 per million input rate, that volume generates a $150 daily input bill, which is inexpensive in absolute terms but still requires monitoring to catch documents that exceed the soft limit before they cause API errors that halt the queue.

$0.30 input and $2.50 output per million tokens

At $0.30 per million input tokens and $2.50 per million output tokens, Flash-Lite carries roughly an eight-to-one output-to-input ratio, similar in shape to most other models, but its absolute rate stays far below what frontier models charge for the same volume. A request with 3,000 input tokens and 3,000 output tokens costs about $0.0084 before search or other add-on charges, a fraction of what most frontier models charge for the same volume of generated content.¹

For applications that produce long outputs such as translations, detailed reports, or code generation, Flash-Lite's low absolute output rate can reduce total costs by around 90 percent compared to frontier models charging $25 to $30 per million output tokens, making it one of the most cost-effective options for generative workloads at scale. Applications with very short outputs benefit less from Flash-Lite's pricing advantage in relative terms, since the input rate is already close to the floor for most providers, which is why understanding your typical input-to-output ratio is essential before choosing Flash-Lite for a production batch.

Sizing output-heavy Flash-Lite jobs

Before batching translations or rewrites, estimate both input and output tokens because the output length drives most of the request cost. A translation task that takes 2,000 input tokens and produces 2,000 output tokens costs $0.0056 per request at Flash-Lite pricing, but the same task on a model charging $5 input and $25 output per million tokens would cost $0.06 per request, roughly an 11x difference that makes accurate output-length estimation essential for choosing the right model before committing to a production batch.

The characters divided by 3.8 estimate for Flash-Lite

Flash-Lite uses Google's model-specific tokenizer, which is not available as a general browser library for every Gemini model, so the browser-side count relies on a character-division formula rather than the exact vocabulary the API applies to your text. The ~ estimate (character count divided by 3.8) follows Google's guidance that Gemini tokens are roughly 4 characters each, and for English prose the variance from the actual API count typically falls in the 10 to 15 percent range, though the gap widens for code and non-Latin scripts.³

Google states the same relationship as 100 tokens equal to about 60 to 80 English words, which is the reference point behind any character-division estimate.⁴

Why the estimation margin costs less at Flash-Lite pricing

Because Flash-Lite is so inexpensive, even a 15% estimation error represents a very small absolute cost difference. Budget conservatively by adding 20% to the estimated token count when projecting spend for large batch jobs. At $0.30/M input, a 20% buffer adds only $0.06 per million input tokens to your projection. This low cost of uncertainty is a genuine advantage: on a model charging $5 per million input tokens, the same 20% buffer adds $1.00 per million tokens to the projection, which means the financial risk of estimation error is roughly 17 times higher on the more expensive model than it is on Flash-Lite.

Budget planning for large batch jobs is therefore simpler on Flash-Lite because the 20 percent buffer you add for safety adds only a few cents per million tokens rather than a dollar or more on frontier models. This low financial risk of approximation is what makes a character-division estimate acceptable for Flash-Lite workloads where the same margin would be unacceptable on a model charging premium rates per token.

Verifying Flash-Lite token estimates with Google AI Studio

For developers who need exact token counts rather than the character-division estimate this tool provides, Google's token docs recommend calling the countTokens API method before sending input to check request size.³ Google describes that same method as the way to stop a request from exceeding the model context window before it is sent.⁵ Paste your prompt into the AI Studio interface and select Gemini 3.5 Flash-Lite as the model: the interface displays the exact token count before you send the request, giving you a reliable pre-flight check at no cost.

In code, the Google Generative AI SDK exposes a countTokens method: const result = await model.countTokens({ contents: [{ parts: [{ text: yourText }] }] }). This returns the exact token count the API will charge, making it suitable for pre-flight validation in production pipelines. For workloads where billing accuracy matters more than fast browser-side estimation, integrate SDK-based counting into your request handler and use this tool for quick sizing decisions only.

When Flash-Lite still wins despite a real output premium

Flash-Lite's output rate is proportionally higher than its input rate, the same shape most models follow, so the advantage comes from the absolute price floor rather than a flat ratio. Most frontier models charge $5 or more per million input tokens and $25 or more per million output tokens, a gap Flash-Lite's $0.30/$2.50 pricing undercuts by a wide margin regardless of your output length. A request with 2,000 input tokens and 2,000 output tokens on Claude Sonnet 5 costs $0.004 input plus $0.020 output: $0.024 total. The same request on Flash-Lite costs $0.0006 input plus $0.005 output: $0.0056 total, roughly a 4x cost reduction for equal-length input and output.

The gap widens further against models still charging frontier rates. Translation tasks, document rewriting, and content expansion workflows typically produce outputs comparable to or longer than inputs. For these workloads, Flash-Lite's combination of a 1M-token context window and sub-dollar per-million pricing is difficult to match among currently available models. Run your own input-to-output ratio through this tool before committing a workload, since the size of the advantage still depends on how much of the cost sits in generated output rather than the prompt itself.

When to use this

Use this when estimating costs for high-volume, output-heavy workloads like translation, content generation, or document rewriting. Flash-Lite lists next to Gemini 3.1 Pro in the same grid, so a quick paste shows exactly how much of that roughly sevenfold price gap applies to your actual prompt. You should also check token counts when sending very long documents to Flash-Lite to confirm they fit within the 1M context limit.

Examples

10,000 documents. Average: 1,500 tokens each. Average translation output: 1,500 tokens.

Total per document: ~$0.0042. For 10,000 documents: ~$42. Compare to Sonnet 5 at $2+$10/M: ~$180.

Marketing team needs 500 blog posts rewritten. Each: 3,000 tokens in, 3,000 tokens out.

Flash-Lite cost: ~$4.20. A model charging $5/M input + $25/M output for the same task: ~$45.

Grok 4 Token Counter

Built for developers testing xAI's frontier model, Grok 4 pairs a 256,000-token context window with API access for reasoning and real-time information tasks.¹

Grok 4 is developed by xAI, Elon Musk's AI company. At $3.00 per million input tokens and $15.00 per million output tokens, its pricing is listed consistently across current pricing catalogs.² The tokenizer is not available as a browser library, so this tool uses the standard character-division estimate.

A 256K window, roughly 190,000 words

Grok 4 processes up to 256,000 tokens per request, which is equivalent to roughly 190,000 words of plain text and represents a meaningful but finite working memory for complex multi-step tasks.¹ Current pricing catalogs consistently list the same 256K context window paired with $3.00/$15.00 per million token pricing across all providers.³ For conversational tasks, coding assistance, and document analysis under 200 pages, this window is generally adequate. The practical constraint becomes apparent only when loading large codebases, extended conversation histories, or multi-document analysis workflows that push past the 200K mark.

How 256K tokens compares to common content sizes

Compared to the 1M-token models at similar price points, Grok 4's context limit is a real consideration. The amber warning at 75% (192,000 tokens) is reached relatively quickly when pasting large documents. At that point, review which context is still needed for the current query rather than assuming the remaining 25% is sufficient headroom. A 50-page document averaging 665 tokens per page consumes 33,250 tokens, which is roughly 13% of Grok 4's 256K window; loading three such documents plus a system prompt and conversation history can push the assembled prompt past the 50% mark, leaving less room for the response than developers typically expect from a premium-priced model.

$3 input and $15 output, level with Claude Sonnet 4.6

Grok 4 charges $3.00 per million input tokens and $15.00 per million output tokens, which matches Claude Sonnet 4.6 exactly on both input and output price and makes the two models directly comparable on a pure cost-per-token basis, with no per-token price difference to factor into your model selection decision.² The practical difference lies entirely in context window capacity: Sonnet 4.6 offers four times more context (1M vs 256K) at the same per-token price, which means any workload exceeding 256K tokens must use Sonnet or a larger-context model regardless of quality preferences, making context capacity the binding constraint rather than cost.

For applications that consistently stay within 256K tokens across all their requests, both models cost the same per request, so the decision reduces entirely to model quality on your specific task type, the quality and availability of each provider's API in your target region, and which SDK ecosystem your team prefers to build integrations around, including the maturity of documentation and community resources available for each platform.

Comparing Grok 4 against same-priced alternatives

When Grok 4 and another model share a price point, use context usage patterns and task fit as the deciding factors before you commit a production batch to either one. Claude Sonnet 4.6 offers four times more context at the same $3/$15 per million token pricing, which means any workload that exceeds 256K tokens must use Sonnet or a larger-context model regardless of quality preferences; for tasks that fit within 256K, the decision reduces to which model produces better output on your specific task type, since the per-token cost is identical.

Estimating Grok 4 tokens without a published tokenizer

xAI has not published Grok 4's tokenizer as a browser-compatible library, which means the count this tool provides is an approximation rather than an exact match for what the API will bill. The ~ estimate (character count divided by 3.8) applies the same formula used for Gemini, MiniMax, and other models without published browser tokenizers. For English text, expect 10 to 15 percent variance from the actual API token count, though the gap widens for code-heavy or non-Latin content. That divergence is large enough that providers now expose a dedicated token-counting endpoint instead of asking developers to estimate: Anthropic documents that a tokenizer change alone can move a prompt roughly 30 percent higher than on earlier models, with the exact increase depending on the content.⁴

Given Grok 4's 256K context limit, this approximation margin leaves a meaningful zone of uncertainty near the context ceiling that you need to account for in your prompt planning. Add a 20% buffer to the displayed estimate before concluding a prompt comfortably fits within the window. A 15% overcount on a 230,000-token estimate could mean the actual prompt exceeds the 256K limit, producing an API error.

Buffering Grok 4 estimates before sending

Because Grok 4 has less room than 1M-context models, use the 75% warning as a hard planning checkpoint. If a prompt is near 190,000 estimated tokens, trim or switch models before paying for a request that may fail. Adding a 20% buffer to the displayed estimate means treating a 160,000-token prompt as if it were 192,000 tokens, which keeps the request safely within the 75% planning zone even if the actual API count runs higher than the browser approximation suggests; this buffer is especially important for Grok 4 because the character-division estimate can diverge by 10 to 15 percent from the actual count, and the smaller context window leaves less room for that variance.

Treat the 75 percent warning as a hard stop during load testing, because a prompt that passes at 190,000 estimated tokens can still breach the 256K window once the actual API count runs high and the request is rejected after you have already paid for input. Scheduling the buffer check before the request leaves your service turns a possible API error into a local planning decision you control.

Context window planning for Grok 4 production workloads

Context window planning is more urgent for Grok 4 than for 1M-context models at the same price. At 256K tokens, a 100-page PDF with approximately 66,500 tokens consumes 26% of the available context before adding a system prompt or conversation history. A conversation thread that accumulates 200 turns at 600 tokens per turn reaches 120,000 tokens, occupying 47% of Grok 4's window versus only 12% of Claude Sonnet 4.6's. Note that the window is consumed by more than the visible transcript: the system prompt, tool definitions, tool results, attached images, and the model's own output all count toward the limit, so a thread can reach the ceiling well before the message list suggests.⁵ The same guidance adds that accuracy and recall degrade as the token count grows, a pattern vendors call context rot, which is the strongest argument for trimming a 190K-token prompt rather than simply hoping the request fits.

For applications that must scale to long documents or extended sessions, evaluate context window capacity alongside per-token price. The 4x difference in context capacity between Grok 4 and Claude Sonnet (256K vs 1M) at identical pricing makes context window size the decisive factor for any workload that routinely exceeds 200,000 tokens.

Comparing Grok 4 and Claude Sonnet 4.6 side by side

At identical input and output pricing ($3/M and $15/M respectively), Grok 4 and Claude Sonnet 4.6 cost the same per token but offer different context capacity. Paste your typical prompt into this tool and compare the context bar percentages: at 100,000 tokens, Grok 4's bar shows 39% while Sonnet's shows only 10%. This visual comparison makes the capacity difference tangible before you choose a model.

For tasks that stay below 100,000 tokens, Grok 4 and Claude Sonnet 4.6 are functionally equivalent on context, and the decision reduces to quality differences on your specific task type. Short-context reasoning, code completion, summarization of moderate-length documents, and standard conversational chat all operate well within Grok 4's 256K ceiling. Watching a live Grok-4-versus-Sonnet context gauge render both bars at once makes the capacity gap visible at a glance rather than something you have to calculate, and the context window limitation only becomes a real constraint when individual prompts regularly approach or exceed that threshold.

When to use this

Use this to check whether your Grok 4 prompt fits within the 256K context window before sending. You should add a 20% safety buffer to the displayed count near the limit, and compare against Claude Sonnet 4.6 pricing if you need more context for the same cost.

Examples

Medium-sized project (2,000 lines Python): ~12,000 tokens. Bug report + instructions: 500 tokens.

Total: ~12,500 tokens, 5% of Grok 4's context. Input cost: ~$0.038.

Large codebase: 230,000 tokens. System instructions: 1,500 tokens. Total: ~231,500 tokens.

Context usage: ~90%, inside the red warning zone. Consider switching to a 1M-context model for this workload.

DeepSeek V4 Pro Token Counter

With a 1M-token window and low cache-miss input pricing, DeepSeek V4 Pro targets long-context pipelines where budget pressure is real.

Token estimates use cl100k_base as a practical approximation. The cl100k_base encoding is used by OpenAI's GPT-3.5 and GPT-4 model families, so this tool can reuse a familiar tokenizer for a quick estimate.¹ DeepSeek's own token guidance says actual counts vary by model and should be verified from API usage results, so the estimate is marked with ~.² DeepSeek V4 Pro supports a 1M-token context window and is priced at $0.435 per million cache-miss input tokens and $0.87 per million output tokens.³

A 1M window for long-context analysis

DeepSeek V4 Pro is designed for long-context analysis at competitive rates, making it attractive for developers who need to process large prompts without jumping to the highest-priced frontier models. Its 1M-token context window provides substantial capacity for document-heavy workloads, research pipelines, and extended conversation histories that would exceed the limits of smaller-context models.

Conversation applications that accumulate history still need to monitor total context across all turns, particularly when system prompts are verbose or when retrieved documents add significant overhead to each request. At 95% context usage (950,000 tokens), the risk of truncation or rejection is real and can disrupt active user sessions. Set a soft limit at 75% of the context window and prune or summarize the oldest context before crossing that threshold, so the model always has adequate headroom for its response.

Setting a DeepSeek V4 Pro soft limit for chat history

A 75% soft limit leaves about 250,000 tokens for the model response and safety margin. In chat products, apply that limit after adding the system prompt and retrieved context, not only after the user message arrives. For applications with verbose system prompts exceeding 5,000 tokens, the effective context available for conversation history shrinks proportionally, which means the soft limit should be calculated against the remaining window after the system prompt is accounted for rather than against the full 1M ceiling.

$0.435 input and $0.87 output per million tokens

DeepSeek V4 Pro costs $0.435 per million cache-miss input tokens and $0.87 per million output tokens, a two-to-one output-to-input ratio.³ This pricing is notably lower than most frontier models. For comparison, Claude Sonnet at $3/$15 costs about 6.9x more on input and 17.2x more on output per million tokens.⁴

For teams running high-volume pipelines where DeepSeek's quality is sufficient, the cost difference over a month of production traffic is substantial. A pipeline processing 500,000 requests per day with 3,000 input tokens and 3,000 output tokens costs roughly $27,000 per day on Sonnet and $1,957.50 per day on DeepSeek V4 Pro, a saving of about $25,042.50 daily.

Projecting DeepSeek costs before scale

Use the displayed INPUT $ and OUTPUT $ columns on representative prompts before you commit to a daily batch volume. A pipeline processing 100,000 requests per day with 5,000 input tokens and 2,000 output tokens costs roughly $324 per day on DeepSeek V4 Pro compared to $5,100 per day on Claude Sonnet 4.6, a saving of $4,776 daily that adds up to over $1.7 million per year from model selection alone.

Counting DeepSeek tokens with cl100k_base

This tool estimates DeepSeek V4 Pro tokens with cl100k_base, the tokenizer family used by OpenAI's GPT-3.5 and GPT-4 model families, which provides a close but not perfect match for DeepSeek's actual vocabulary.¹ DeepSeek's own token guidance says conversion ratios vary by model and actual counts come from API usage results, so the estimate is a practical approximation rather than an exact billing-grade count.²

DeepSeek ships its own tokenizer alongside the V4 weights, which is why a published cl100k_base vocabulary can only approximate it.⁵

How error rates differ for English versus Chinese content

For English text, expect less than 5% error. For production billing estimates, add a 10% buffer to the displayed count when projecting costs. For Chinese-heavy prompts, the error may exceed 10–15% because DeepSeek's tokenizer handles Chinese characters differently from the English-heavy approximation used here. DeepSeek was trained extensively on Chinese data, and its native tokenizer likely encodes Chinese characters more efficiently than cl100k_base predicts, which means the actual token count for Chinese content could be significantly lower than the estimate, making the tool's cost projection a conservative overestimate rather than an undercount.

For Chinese-heavy production traffic, lean into the conservative side of the estimate rather than trimming the buffer, because the native DeepSeek tokenizer encodes Chinese more efficiently than cl100k_base predicts and the real count may land well below the projection. Logging actual billed counts from the API for a sample of Chinese prompts calibrates your buffer so you stop over-provisioning context once the real density is measured.

Calibrating the cl100k approximation for your DeepSeek content

When your DeepSeek V4 Pro prompts consist primarily of standard English prose, the cl100k_base count this tool provides lands within 5% of the actual API count. That precision is sufficient for cost budgeting, prompt sizing, and context window feasibility checks. Add a 10% buffer to the displayed count when projecting billing for large batch workloads to absorb the approximation error.

For critical cost projections, verify the approximation against actual API usage data by running a representative sample through DeepSeek V4 Pro and comparing the billed token count to this tool's estimate. If your sample consistently shows a 12% or higher discrepancy, adjust your internal multiplier to match. Track this calibration factor in your cost modeling and apply it to all future DeepSeek billing projections.

Comparing DeepSeek V4 Pro to Claude Sonnet 4.6 at scale

At $0.435 per million cache-miss input tokens, DeepSeek V4 Pro costs about 85.5% less than Claude Sonnet 4.6 on input and about 94.2% less on output ($0.87 vs $15 per million). For a pipeline processing 500,000 requests per day with 3,000 input tokens and 3,000 output tokens, the daily difference is roughly $25,042.50 before provider-specific discounts or cache behavior.

The trade-off is ecosystem maturity: Claude Sonnet has broader SDK support, more community examples, and well-documented stability commitments. DeepSeek V4 Pro offers competitive reasoning quality at a substantially lower price point. Paste your actual system prompt and a representative user message to see DeepSeek and Sonnet input costs side by side in one pass instead of two separate lookups before committing to a production choice.

When to use this

Use this to estimate DeepSeek V4 Pro token counts and input costs before scaling a pipeline. You should apply a 10% buffer on top of the displayed count for accurate billing projection.

Examples

Repository context (Python, 2,500 lines): ~15,000 tokens. Analysis instructions: 600 tokens. Total: ~15,600 tokens.

Input cost: ~$0.0013. Compare to Claude Sonnet for the same prompt: ~$0.047.

5,000 documents, average 800 tokens each. Average response: 400 tokens.

Daily cost: ~$1.74 input + ~$1.74 output = ~$3.48 total. A budget-conscious option for batch processing.

Qwen 3.5 Plus Token Counter

A 1M-token context length and low input price make Qwen 3.5 Plus attractive for large-document and multilingual workloads.¹

Alibaba identifies Qwen3.5-Plus as the hosted version of Qwen3.5-397B-A17B. At $0.30 per million input tokens and $1.80 per million output tokens, it is one of the lowest-cost 1M-context options in current pricing catalogs.² The tokenizer is not the same as OpenAI's cl100k_base library, so this tool uses a close approximation rather than an exact count.

1M context by default on the hosted Plus model

Qwen 3.5 Plus supports a 1,000,000-token context window, and Qwen's model card describes the hosted Plus variant as having 1M context length by default.¹ For long-context applications where frontier reasoning is not required, such as large-document analysis, extensive conversation history, or bulk summarization, Qwen's combination of capacity and price is hard to match among currently available models. The red warning threshold at 95% of 1M (950,000 tokens) is the practical ceiling to avoid API errors, so plan your context allocation to stay well below that mark.

Using Qwen 3.5 Plus for large-document inputs

For long-document analysis, count the document, system prompt, and user question together before choosing a chunking strategy. If the total is below 75% of the window, Qwen can usually handle the request directly; above that, split retrieval chunks or summarize older context. A 400,000-token legal brief combined with a 2,000-token system prompt and a 500-token user query totals 402,500 tokens, which consumes 40 percent of Qwen 3.5 Plus's 1M context window and leaves nearly 600,000 tokens for the model to generate a detailed analysis.

$0.30 input and $1.80 output per million tokens

At $0.30/M input and $1.80/M output, Qwen 3.5 Plus matches MiniMax M2.7 on input price parity while charging more on output, so the six-to-one output-to-input ratio is the key factor in any cost comparison.² This ratio means output tokens cost substantially more than input tokens, and whether that matters depends on your specific workload's input-to-output proportions.

For workloads that generate short outputs relative to long inputs, such as classification, extraction, or yes/no answers, Qwen's pricing is extremely competitive because nearly all the cost sits in the input. For generative tasks that produce long outputs, the output price matters more: a 2,000-token response at $1.80/M costs $0.0036, while the same output on Claude Haiku at $5/M costs $0.010, making the choice between models dependent on your typical response length.

Matching Qwen output length to the task

For extraction and classification, keep the response format compact so the six-to-one output ratio does not erase the low input price advantage. A classification task that returns a single 20-token label costs $0.000036 in output at Qwen's $1.80 per million output rate, but the same task with a verbose 500-token explanation costs $0.0009 in output, a 25x increase that eliminates much of the cost advantage Qwen's $0.30 per million input rate provides and illustrates why response format design is as important as model selection for controlling API costs.

The Qwen BPE tokenizer versus cl100k_base

Qwen3.5 uses a Hugging Face Transformers Qwen3_5Tokenizer with a BPE model, custom pretokenization regex, byte-level decoding, and NFC normalization, giving it a vocabulary that differs from OpenAI's published tokenizers.³ That is not the same as OpenAI's cl100k_base tokenizer, so the ~ indicator reflects approximation rather than exact hosted-token parity.³

For English, the error is typically under 5 percent, which is sufficient for cost budgeting and context window planning at most production scales. For Chinese content, Qwen's tokenizer likely handles it more efficiently than cl100k_base predicts, so the estimate may overcount for Chinese-language prompts and your actual API bill could be lower than the tool projects. Actual API token counts are the ground truth for billing, so verify critical projections against real usage data.

When the 6:1 output ratio matters most

When your pipeline generates responses comparable in length to the input, the six-to-one output-to-input ratio becomes the dominant cost factor, and the low input rate alone no longer determines the total per-request cost. A classification task with a 500-token prompt and a 20-token response benefits greatly from Qwen's $0.30/M input rate, because input carries nearly all the cost for that request. Yet a summarization task with a 500-token prompt and a 500-token summary pays six times more per output token than per input token, and at scale that ratio matters far more than the absolute per-token rate.

For pipelines where output length approaches or exceeds input length, compare Qwen 3.5 Plus to Gemini 3.1 Flash-Lite, which Google lists at $0.25 per million input tokens and $1.50 per million output tokens, below Qwen's rate in both directions.⁴ For equal-length input and output, Flash-Lite's lower rates undercut Qwen's pricing on both sides. Paste your typical prompt and a representative response into this tool and compare both models side by side before committing to a production choice.

Projecting Qwen 3.5 Plus costs for large-volume workloads

For high-volume pipelines where input cost is the primary constraint, Qwen 3.5 Plus offers a measurable and often dramatic advantage over many other 1M-context models that charge significantly more per token for the same workload. At 10,000 requests per day with a 2,000-token average input, the daily input cost is only $6.00, compared to $20 on Claude Haiku, $60 on Claude Sonnet, and $100 on Claude Opus or GPT-5.5, which means the annual savings from choosing Qwen over Sonnet alone can exceed $19,000 for this single workload configuration.⁵

Building a cost floor estimate before deploying

To project your specific workload accurately, start by building a Qwen 3.5 Plus cost floor against Haiku, Sonnet, and Opus: paste your system prompt and a representative user message, then read the INPUT $ column for Qwen 3.5 Plus. Multiply that per-request cost by your expected daily request count, then add your estimated output cost by multiplying average output tokens by $0.0000018 per token. Run this calculation before deploying to confirm the cost advantage holds at your actual request volume and response length, not just in the abstract comparison.

Run the same calculation across a few representative request shapes rather than a single average, because a workload with occasional long inputs can hide a large share of spend inside a few outlier requests that the daily mean smooths over. Validating the floor at your real volume is what keeps the headline annual savings from collapsing once production traffic replaces the tidy sample.

When to use this

Use this when building cost-sensitive applications on Qwen 3.5 Plus that process large amounts of text. The same grid lists MiniMax M3 and DeepSeek V4 Pro right next to it, so you can compare all three low-cost long-context options on the same prompt in one pass. You should verify the 1M context limit is not approached when loading full documents, and add a 10% buffer to the estimate when projecting billing costs.

Examples

100,000 product descriptions, each 400 tokens. Extraction instruction: 300 tokens. Response per item: 100 tokens.

Input cost: $0.021 per 100 items. For 100,000 items: ~$21 input + ~$5.40 output = ~$26.40 total.

Full legal brief (600 pages): ~450,000 tokens. Question: 100 tokens. Estimated total: ~450,100 tokens.

Input cost: ~$0.135. That's 45% of the 1M context at the lowest price among 1M-context models here.

Kimi K3 Token Counter

Kimi K3 jumps to a 1,048,576-token context window, moving Moonshot AI's flagship from a mid-context option into the same tier as the largest models this tool tracks.¹

The price moved with it. Kimi K3 lists at $3.00 per million input tokens and $15.00 per million output tokens, roughly three times what Kimi K2.6 cost.² Its tokenizer is not available as a browser-side tokenizer library, so this tool uses the standard character-division estimate and marks the result with ~.

1,048,576 tokens, four times the K2.6 window

Kimi K3 supports a context window of 1,048,576 tokens per request, a fourfold jump from K2.6's 262,144-token ceiling.¹ Workflows that previously had to chunk or summarize before sending to Kimi, entire codebases, long policy documents, or hours of transcripts, now fit in a single request the way they already do on the largest Claude and Gemini models.

At 1,048,576 tokens, the context fits roughly 795,000 words, the equivalent of several long novels loaded into one call. The amber warning at 75% still leaves a meaningful buffer: roughly 262,000 tokens of headroom remain, which happens to be about the same size as K2.6's entire context window. A conversation thread averaging 600 tokens per turn now reaches the 80% mark at around 1,400 turns, versus roughly 350 turns on K2.6.

Retrieval prompts that used to strain K2.6 now fit easily

A RAG pipeline that retrieves 10 chunks averaging 2,000 tokens each consumes 20,000 tokens on retrieval alone; combined with a 1,000-token system prompt and a 200-token user query, the assembled prompt reaches 21,200 tokens. On K2.6 that was over 8% of the available context on every request. On K3, the same prompt uses roughly 2% of the window, leaving far more room for retrieval sets that used to require trimming.

$3 input and $15 output, triple the K2.6 price

Kimi K3 lists at $3.00 per million input tokens and $15.00 per million output tokens, a five-to-one output-to-input ratio and roughly triple what K2.6 cost on both directions.² The jump moves Kimi out of the mid-tier bracket it occupied alongside Claude Haiku 4.5 and into the same price range as Claude Sonnet 5, though Sonnet 5 currently undercuts it at $2.00/$10.00 during its introductory pricing window. Moonshot billed K3 as its most capable release to date, an open-weight model whose benchmark results trail only Claude Fable 5 and GPT-5.6 among current frontier models, positioning that lines up with the shift away from K2.6's budget-tier pricing.³

Compared to Claude Haiku 4.5 ($1/$5, 200K context), Kimi K3 now costs three times as much per token but offers more than five times the context. Compared to Sonnet 5 ($2/$10, 1M context), Kimi K3 is the pricier option for a context window that is only marginally larger. The calculus for choosing Kimi shifted from "cheap long-context option" to "does the model quality justify a premium over Sonnet-tier alternatives for this workload."

Where Kimi K3 still makes sense at the new price

Kimi remains a reasonable choice for teams already built around Moonshot's API and tooling, or for workloads where evaluation testing shows K3 outperforming Sonnet-tier models on a specific task. For a fresh model choice made purely on context-per-dollar grounds, weigh Sonnet 5 and Gemini 3.6 Flash directly against K3 using this three-way Kimi K3 pricing lineup before committing.

If your evaluation shows no meaningful quality gap between K3 and Sonnet-tier alternatives, the cost difference favors switching for new projects. Existing Moonshot integrations don't need to migrate on pricing alone: the context increase is a genuine capability gain even at the higher rate, and switching providers carries its own integration cost that a modest per-token premium may not justify.

The character count approximation for Kimi K3

Moonshot AI has not released Kimi K3's tokenizer as a browser-compatible library, so this tool cannot run the exact vocabulary the API uses for tokenization. This tool uses the same ~character/3.8 approximation as the other non-published tokenizers on this page, and for English text the estimate is accurate to roughly 10 to 15 percent relative to the actual API count.

Kimi's tokenizer likely handles Chinese and other Asian language text differently than the character-division formula assumes, since Moonshot AI is a Beijing-based Chinese AI company founded in 2023, and the model has strong multilingual training that optimizes for East Asian scripts.⁴ For non-English content, the estimate may carry higher error than the 10 to 15 percent range. Run a calibration test against the Kimi API on a sample of your production content to measure your actual characters-per-token ratio before finalizing cost projections.

Estimating tokens for mixed Chinese-English Kimi K3 prompts

Estimating tokens for prompts that mix Chinese and English content is less reliable with the character-division formula than for English-only text. Moonshot AI developed Kimi K3 with strong multilingual training, and its tokenizer likely encodes Chinese characters more efficiently than the ~character/3.8 estimate assumes. Chinese characters are typically single tokens in models with multilingual training, while the formula treats them identically to Latin characters for the count calculation.

For prompts that include significant Chinese content, run a calibration test by submitting a sample to the Kimi API and comparing the billed token count to this tool's estimate. If the actual count is consistently 15-20% lower than the displayed estimate for Chinese-heavy content, your Kimi workloads are less expensive than the tool suggests. Note the calibration factor and apply it when projecting costs for Chinese-language batch workloads, which matters more now that per-token cost has tripled.

Why the 1M-token ceiling changes cost planning

With a 1,048,576-token window, the practical constraint on most Kimi K3 workloads shifts from context capacity to per-token cost, the reverse of the situation under K2.6. At the 90% threshold, you have roughly 943,700 tokens of input headroom, more than enough for a full document plus extensive retrieval, so the context bar in this tool is unlikely to turn red for anything short of a genuinely enormous prompt.

Budgeting for K3's higher per-token rate

Because the ceiling is no longer the binding constraint, cost becomes the lever to watch. A 500,000-token research corpus that comfortably fit within K2.6's context only in pieces now loads in one request, but at $3.00/M that single request costs $1.50 in input alone, versus roughly $0.34 at K2.6's old rate. For batch workloads processing many such documents daily, that difference compounds quickly, so re-running your cost projections after switching to K3 is worth doing before scaling a pipeline that was tuned against the old pricing.

In one published test, a 95-token prompt to generate an SVG image produced 16,658 output tokens, 13,241 of them reasoning tokens, for a total cost of about 25 cents. Independent testing has also found K3 currently offers only a single reasoning-effort level, and that reasoning tokens, which are billed as output, can run into the thousands even for a short prompt, so budget for output cost well beyond the length of the visible response alone.⁵

When to use this

Use this to estimate Kimi K3 token counts and compare its cost against Claude Sonnet 5 and Gemini 3.6 Flash before committing a workload to Moonshot's API. Kimi K3 lists next to Sonnet 5 and Gemini 3.6 Flash in the same grid, so you can weigh the new pricing against those alternatives on your own prompt instead of the numbers in this guide. You should apply a 15% buffer on the displayed estimate when projecting costs for large batches of real-world content.

Examples

A 200-page technical specification: ~150,000 tokens. Analysis prompt: 500 tokens. Total: ~150,500 tokens.

Context usage: ~14% of the 1,048,576-token window. Input cost estimate: ~$0.45 at $3.00/M. Room for a detailed response remains.

20 turns of research conversation averaging 600 tokens each. Total history: ~12,000 tokens.

At 12,000 tokens, you are using roughly 1% of the context window. Average cost per turn across the session: ~$0.019 input at $3.00/M.

MiniMax M3 Token Counter

M3 keeps M2.7's pricing but replaces its roughly 205,000-token context window with a full 1,000,000 tokens, a change that moves MiniMax from a modest-context option into the same context tier as Claude Sonnet 5 and Gemini 3.6 Flash.¹²

MiniMax lists M3 at $0.30 per million input tokens and $1.20 per million output tokens, unchanged from M2.7. The tokenizer is not published as a browser library, so this tool approximates using the standard character-division formula. MiniMax is a Chinese AI company, and M3 now targets long-context tasks at the same price point that used to buy a fraction of the context.³

1M tokens, up from about 205K on M2.7

MiniMax M3 processes up to 1,000,000 tokens per request, up from roughly 205,000 on M2.7.² For workflows that need to hold large amounts of context while keeping costs well below frontier-model pricing, M3 is now one of the strongest combinations of context and price available on this page. That jump changes what kinds of workloads make sense on M3 without requiring any change to the pricing you already budget for.

Because the tokenizer is not exact here, add a 15 to 20 percent buffer to the displayed token count when designing prompts that approach the context limit, which absorbs the variance between the browser estimate and the actual API count. MiniMax notes that token-to-character ratios vary by usage scenario, so the character-division estimate is a planning figure rather than a billing guarantee, and you should verify critical projections against real API usage data.

Buffering MiniMax M3 prompts near the 1M limit

Treat 800,000 to 850,000 tokens as the practical planning zone for M3. That range leaves room for the response and absorbs tokenizer variance when the prompt includes multilingual text, tables, or generated JSON. A prompt the tool estimates at 900,000 tokens might actually land higher on the API side if it contains significant Chinese content or dense formatting, which is worth checking before it pushes the request toward the 1M ceiling.

$0.30 input and $1.20 output per million tokens

MiniMax lists M3 at $0.30 per million input tokens and $1.20 per million output tokens, the same rate M2.7 charged.¹ The output-to-input ratio of 4x is standard for a mid-size model. At these rates, MiniMax M3 is 94% cheaper on input than Claude Opus 5 ($5/M) and 95% cheaper on output ($25/M), while now matching Opus 5's 1M-token context window.⁴

For developers who need a large context window and are cost-sensitive, M3's pricing is among the most competitive on this page, undercutting most alternatives by a wide margin at comparable context capacity, a gap that widened considerably now that the context ceiling no longer caps the comparison at 205K. The practical tradeoff is using a model from a less widely documented provider, with fewer community resources and SDK integrations than Claude, GPT, or Gemini, which means more upfront evaluation work before committing to a production deployment.

Why MiniMax M3 counts are approximate

MiniMax has not published M3's tokenizer as a browser-compatible library, so this tool cannot replicate the exact vocabulary the API applies to your specific text with perfect accuracy, and the browser-side count relies on a generic approximation rather than the model's actual vocabulary. Instead, this tool uses the standard ~character/3.8 approximation, which gives results within 10 to 15 percent of the actual API count for typical English prose, though the variance grows for non-English content. MiniMax explicitly notes that token-to-character ratios vary significantly by usage scenario and that English-token estimates derived from the generic 3.8 divisor are only approximate at best, so calibration against actual API usage data from your own content is the safest way to build a reliable cost model for production workloads. MiniMax also exposes an API endpoint that returns the input token count for a request without invoking the model, which is the exact-count alternative to a browser estimate.⁵

For prompts mixing Chinese and English, the character estimate may diverge more from the actual API count than for English-only content, since MiniMax's tokenizer likely handles those scripts differently from the simple character-division formula. Run a calibration pass on a sample of your production content to measure the actual characters-per-token ratio before finalizing budget projections for mixed-language workloads.

Calibrating MiniMax estimates for multilingual prompts

If your production content mixes Chinese and English, test a sample against the API and apply your observed multiplier to future cost projections. A calibration test with 50 to 100 representative prompts is usually sufficient to establish a reliable characters-per-token ratio for your specific content mix; run the sample through the MiniMax API, compare the billed token counts against the character counts, and use the empirical ratio instead of the generic 3.8 divisor for all subsequent cost projections.

Use cases where MiniMax M3 offers the strongest cost advantage

For developers building long-context applications on a tight budget, MiniMax M3 now offers one of the strongest price-per-context-token ratios available, at a full 1M-token window instead of the roughly 205K M2.7 offered. At $0.30 per million input tokens, the cost of using the full 1M-token context on M3 is about $0.30. The same full-context request on Claude Sonnet 5 costs $2.00 at its current introductory rate, roughly 6.7x higher for an identical token count. For applications that routinely push against the context ceiling, this difference is substantial.

Workloads particularly well-suited to M3 include large codebase analysis where many files must be loaded simultaneously, legal document review where full contracts must be in context for cross-reference, and long-form content pipelines where entire documents are processed at once, all tasks that used to require chunking on M2.7's smaller window. Evaluate M3's quality on your specific task before committing; the price advantage is irrelevant if the output quality does not meet your requirements.

Evaluating MiniMax M3 before large-scale deployment

When you rely on a model from a less widely documented provider, a few operational considerations matter more than with established providers. MiniMax M3 has fewer community resources, SDK integrations, and independently published benchmarks on specific task types compared to Claude, GPT, or Gemini. This means you will do more direct experimentation and less searching for existing community guidance.

Setting a quality threshold before committing a workload

Before committing a high-volume workload to MiniMax M3, run 50 to 100 representative prompts through the API and score the outputs against your quality criteria. Compare those scores to outputs from a more established model at higher cost. If M3 meets your quality bar on 90% or more of your test cases, the cost savings justify the ecosystem trade-off.

Build in a fallback that routes requests to a backup model if M3's API shows elevated error rates, since smaller providers sometimes have less predictable reliability under load. Establishing this quality gate before deployment prevents the costly scenario where a full batch job completes on M3 only to reveal systematic quality issues that require reprocessing the entire dataset on a more expensive model. A well-designed evaluation pass measures not just output correctness but also consistency across similar inputs, because a model that produces correct answers on 90% of cases but wildly varying answers on the remaining 10% can introduce downstream errors that are harder to detect than uniformly lower-quality output.

Accounting for the tokenizer approximation in cost projections

Token count accuracy matters more for MiniMax M3 than for models with exact browser tokenizers because the character-division estimate can diverge by 15-20% for non-English content. MiniMax notes that token-to-character ratios vary by usage scenario, so actual API usage is the safest basis for cost projections. If you estimate 200,000 tokens per day but the actual count is 240,000, your actual daily cost is $0.072 rather than $0.06 at MiniMax's listed input rate.

For the most reliable projections, run a calibration pass on a sample of your real production content before finalizing your cost model. Send 1,000 representative requests through M3 and collect the actual billed token counts from the API response's usage field. Divide the total actual tokens by the character count of the same content to find your actual characters-per-token ratio, and use that ratio instead of 3.8 for all future M3 cost projections. Running a prompt through MiniMax M3's character-division sanity check before that calibration pass costs nothing and gives you a rough number fast, though the calibration pass against real API usage remains the more reliable figure for a production budget.

When to use this

Use this to check whether a MiniMax M3 prompt fits within the 1M-token context limit and to estimate costs before scaling a pipeline. You should add a 15-20% margin to the displayed estimate when budgeting for production batch workloads.

Examples

Full repository context: 80,000 tokens. Review instructions: 500 tokens. Total: ~80,500 tokens.

Input cost estimate: ~$0.024. Compare to Claude Sonnet 5 for the same input: ~$0.16.

Legal contract (400 pages): ~300,000 tokens. Summary instruction: 200 tokens.

Context usage: ~30% of the 1M-token window. Input cost: ~$0.09. Output summary (1,000 tokens): ~$0.0012.