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Why token counts and costs vary
A token is not a word-count unit. The same text can produce different token counts depending on the model, its encoding, and the language. A pasted-text count may also omit request structure or other inputs, such as roles, tool definitions, schemas, images, and files. OpenAI explains these distinctions in its token-counting guidance.
Costs can diverge even when two models receive the same prompt. Models may tokenize the text differently and generate different amounts of output or reasoning. OpenAI also counts reasoning tokens toward output usage and billing even when those tokens are not visible in the final answer.
How to measure a request’s token use
Count the complete input where possible
For plain text, use the tokenizer and encoding for the target model; OpenAI’s Python example uses tiktoken.encoding_for_model(model). Treat that as a text count, not necessarily a complete count of a structured API request. For a complete Responses API input, OpenAI provides an input-token counting API that accepts structured inputs and includes formatting tokens such as roles and boundaries.
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After the request runs, use the API’s returned usage fields as the measurement for that call. Responses reports input_tokens, output_tokens, and total_tokens. Chat Completions reports prompt_tokens, completion_tokens, and total_tokens. Those observed values are more useful for calibrating estimates than a count of only the text a user can see.
Check how a cross-provider counter counts
Token-counting websites do not necessarily use one method for every model. Some use local tokenizers, some call provider counting endpoints, and some estimate with heuristics when an exact method is unavailable. For example, the published methods from How Many Tokens? and LLM Cost Check describe different approaches by model or provider. Read the stated method and confidence limits rather than treating every displayed count as exact.
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How to estimate the cost of a prompt
Define a representative workload first
Choose the model and endpoint, then describe the real task: language, prompt format, tools or schemas, expected answer length, turns per conversation, monthly call volume, and any image, audio, or other modalities. Use a representative sample of tasks rather than a generic prompt. OpenAI’s guidance recommends considering the total tokens and cost needed to complete the task.
Apply rates to the right usage categories
For a basic text request, estimate input-token cost plus output-token cost:
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(input tokens × input rate) + (output tokens × output rate)
Then add separate charges when they apply, such as cached input, cache writes, image or audio processing, tools, or other provider billing units. Rates and billing categories vary by provider and model, so check the provider’s current pricing information before budgeting. The independent LLM Cost Check methodology notes that its manually transcribed rates can become out of date; third-party price snapshots should not replace current official rates.
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For a monthly estimate, multiply the per-request estimate by expected volume and make the assumed output length and usage mix explicit. A calculator cannot know the actual generated output length before the request runs; treat it as an assumption, then refine it with observed usage.
Include conversation history, retries, and task success
In a stateless API workflow, the application may resend earlier messages on each turn. Count the full input sent on every turn, not just the newest user message. The effect depends on how the application and provider handle conversation state; measure actual usage rather than assuming that each exchange costs the same. The LLM Cost Check methodology describes modeling each turn and summing its costs.
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For a meaningful model comparison, run the same representative tasks, record usage and applicable rates, and track whether each run produces an acceptable result. Include retries and the cost of longer outputs when they occur. A lower price per million tokens does not necessarily mean a cheaper completed task if the model consumes more tokens or needs more attempts.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What estimates can miss
- Hidden or non-text usage: Reasoning tokens may be billed as output even when they are not visible. Images, audio, video, tools, and other features may use separate billing schedules or units.
- Approximate counts: Heuristic counters can be less reliable for code, structured data, non-Latin text, or other atypical input. A third-party methodology’s accuracy claim applies only to its stated models and method.
- Excluded charges: A calculator may leave out modalities, tools, failed requests, retries, embeddings, or fine-tuning. The LLM Cost Check methodology identifies exclusions in its own scope; check provider billing records for the items your workload uses.
- Changing rates: Pricing pages and third-party snapshots change. Verify official rates at the time of a budget decision, and label the date and assumptions behind any estimate.
Is the published table really off by 2.3x?
The “off by 2.3x” figure is not established by the available evidence: no identifiable table, dataset, or reproducible comparison is specified. Without the original table and its assumptions, it is not possible to say which counts were compared, which models or task were used, what counted as a token, or which prices applied. Do not treat 2.3x as a general result about AI token counts or costs.
To check a specific table, obtain its source data and compare the same representative requests using each target model’s counting method. Record actual input and output usage, current rates by category, and the date of the comparison; for multi-turn or tool-using workloads, include history, retries, and relevant extra charges. That makes any discrepancy interpretable rather than just a headline multiplier.
Quick Recap
A practical comparison checklist
- Same representative task set, model endpoint, language, and request structure.
- Observed input and output usage, separated by category where the provider exposes it.
- Documented tokenizer, counting endpoint, or approximation method, with its confidence limits.
- Current input, output, and applicable cache or modality rates, with the rate date.
- Conversation turns, retries, and tool usage included in the workload.
- Task success or quality measured alongside cost, so the comparison reflects cost per useful result.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
