Claude Opus 5 Token Counter

Claude Opus 5 Token Counter: Count Tokens Before You Send

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

Context window and limits

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.

Pricing breakdown

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.

Tokenizer and accuracy

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. 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.