Comparing AI Model Costs by Token Count

Comparing AI Model Costs by Token Count: Token Counter Guide

Only the same prompt makes a cheaper-looking model truly cheaper.

Two models with the same per-million-token price can cost very different amounts for the same text if their tokenizers count that text differently. A model that tokenizes English prose into fewer tokens per word is cheaper to use in practice than its published rate suggests compared to a model with a larger vocabulary that splits the same words into more tokens. Price comparison without token counting is unreliable.

Why token rates alone are not comparable

Consider two models where Model A charges $3/M tokens and Model B charges $3.50/M tokens, making Model B look 17 percent more expensive based on the published rate alone. However, the sticker price does not determine actual cost per request because different tokenizers count the same text differently. If Model A's tokenizer uses 1.1 tokens per word on average and Model B's uses only 0.9 tokens per word because a more compact vocabulary encodes common words into single tokens more often, the same 1,000-word prompt costs 1,100 tokens on Model A but only 900 tokens on Model B, completely flipping the cost comparison.

Consequently, Model B costs less per request despite the higher published rate, which is why vocabulary efficiency matters just as much as the sticker price when you are comparing models for a production workload. The only reliable way to compare true costs is to count tokens against each model's actual tokenizer using your own representative prompts, since small per-word differences compound across long prompts and high-volume workloads into material dollar amounts.

Comparing the same prompt across candidate models

Paste the exact system prompt and representative user messages into the same counter, then focus on reading true per-prompt cost instead of the sticker rate for every model you are evaluating. The cheapest published rate can still lose once the tokenizer efficiency and context limit are factored in, since a model that charges $1 per million tokens but produces 1.5 tokens per word from your specific content costs more in practice than a model charging $1.20 per million tokens that produces only 1.1 tokens per word from that same content.

How to run an accurate cost comparison

The correct method is straightforward but requires discipline to execute properly: collect at least five representative prompts that span the full range of content types and lengths in your actual use case, paste each into a token counter that covers all candidate models, and record the token count and displayed input cost for each before averaging the results across your full sample to smooth out variance between individual items.

Building on this measurement, if your use case generates output, sample some representative responses too and multiply their token counts by the output price for each candidate model, since the total per-request cost (not just input) determines which model is actually cheapest for your specific workload pattern. Output tokens often cost several times more than input, so ignoring them entirely can lead to a completely wrong conclusion about which model saves money at your actual generation-to-input ratio.

Comparing total request cost, not only input cost

For generative workflows, always include representative output lengths in the comparison so the cheapest input rate does not hide expensive long responses that dominate the total bill. A model with a $0.30 per million input rate but a $1.80 per million output rate can end up costing more overall than a model with a $3 per million input rate and a $1 per million output rate if the workload generates outputs that are three times longer than the inputs, because the total per-request cost depends on both rates weighted by the actual input-to-output ratio of your specific workload.

When to prioritize cost versus quality

Model selection rarely reduces to cost alone, yet cost and quality operate on different spectra for different tasks and understanding both dimensions is essential for making a good production choice. Classification, extraction, translation, and short summarization show small quality differences between mid-tier and frontier models, making cost the dominant selection criterion for those task types.

Task categories where the quality gap between tiers is largest

Complex reasoning, multi-step coding, nuanced analysis, and medical or legal interpretation show large quality differences between model tiers, making quality the dominant selection criterion regardless of price. A tiered approach that routes simple tasks to cheap models and complex tasks to premium models optimizes both cost and quality across a heterogeneous workload. The quality gap is most visible on tasks that require maintaining coherence across many reasoning steps, synthesizing contradictory information from multiple sources, or producing outputs that must conform to strict domain-specific formats; for these categories, the difference between a mid-tier and frontier model can mean the difference between an output that requires manual correction and one that is production-ready without human review.

Score each task type against this quality spectrum before committing to a routing plan, because a workload that looks cost-driven on average often hides a small fraction of requests where the frontier model is the only one that produces usable output. Measuring the correction rate per tier tells you exactly where the cheaper model stops saving money and starts costing it in rework.

Routing between models to optimize cost without sacrificing quality

When your application handles tasks that vary widely in complexity, routing each request to the most appropriate model reduces total cost without sacrificing quality on complex tasks. A customer support application where 80% of queries are simple FAQs and 20% require detailed reasoning can route simple queries to Claude Haiku ($1/M input) and complex ones to Claude Sonnet ($3/M input). The blended input cost at that routing ratio is $1.40 per million tokens: 53% cheaper than routing everything to Sonnet¹.

A routing signal can be as simple as a keyword check or as sophisticated as a Haiku-based classification prompt. A keyword check adds no API cost but misclassifies edge cases. A Haiku scoring prompt adds roughly 300 tokens per request but classifies more accurately². Count the classifier prompt tokens in this tool and factor the overhead into your cost model before assuming the routing saves money; a poorly calibrated classifier can negate the savings it was designed to produce.

Accounting for tokenizer differences in cross-model cost comparisons

In cross-model cost comparisons, tokenizer differences affect the result more than most developers expect. The same 1,000-word document produces different token counts on different models: roughly 1,330 tokens on Claude (Anthropic tokenizer), approximately 1,330 tokens on GPT-5.5 (o200k_base), and a different count again on models using cl100k_base or character-division approximations³. These small per-sentence differences compound across long prompts and high-volume workloads.

For a fair comparison, paste your actual prompt text and read the INPUT $ column for each candidate model. This tool applies each model's tokenizer or best available approximation before computing the cost, so the displayed cost already accounts for vocabulary differences. A model that looks cheaper per million tokens in its published rate may cost more in practice if its tokenizer produces more tokens from your specific content type. The cost-per-prompt comparison is more reliable than the cost-per-million-tokens comparison for cross-model selection decisions.

When to use this

Use this when selecting a model for a new API integration: one paste updates the cost column for every model at once, so the side-by-side view this guide describes is a single action, not a spreadsheet you build yourself. You should paste your actual system prompt and a typical user message, then compare the INPUT $ column across all 37 models to find the best cost-to-capability ratio for your use case.

Examples

System prompt: 600 tokens. Average item: 300 tokens. Total input per request: ~900 tokens.

At 900 tokens: Qwen 3.5 Plus $0.00027, Haiku $0.00090, Sonnet $0.00270, Opus $0.00450. Qwen is 17x cheaper than Opus for the same input.

Same 900-token prompt. Task: multi-step financial analysis requiring reasoning accuracy.

Opus or GPT-5.5 may be worth the 17x price premium if classification errors cost more than the per-request price difference.