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To estimate what an AI task will cost, measure representative requests with the target model’s counting method, then check the usage reported after real calls and apply current rates by billing category. A token price table alone cannot predict a bill: tokenization, message structure, output length, conversation history, reasoning, caching, retries and modalities all matter. The specific claim that a published table was off by 2.3× could not be verified against an identifiable table or reproducible comparison, so it should not be treated as a general finding.
Why token counts and price tables can mislead
A token is not a word. The same passage can produce different token counts depending on the model, its encoding and the language. Even a correct count of pasted text may omit parts of the API request, such as message boundaries, roles, tool definitions or structured schemas. Images and files can also affect usage in ways a plain-text counter does not capture.
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Pricing comparisons add another source of error. Providers may bill input and output at different rates, expose separate cached-input rates, or count reasoning tokens as output even when those tokens are not visible in the answer. A model with a lower listed price per million tokens may still cost more for a completed task if it uses more tokens, produces longer answers or needs retries. OpenAI’s token guidance explains why counting and cost estimation need to be model- and request-aware.
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How to measure a representative workload
1. Define the task and request shape
Before comparing models, write down what the application actually sends and expects. Include the model and endpoint, language, prompt structure, tools or schemas, expected answer length, turns per conversation, monthly volume and any image, audio or other modalities. Use representative tasks rather than one generic prompt; a useful comparison includes both the usage and the quality or success of the result.
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2. Count the complete request where possible
For plain text, OpenAI recommends selecting the target model’s encoding, for example with tiktoken.encoding_for_model(model). Treat that as a text estimate, not necessarily a complete API request count. For a complete Responses request, OpenAI provides an input-token counting API that accepts structured inputs and accounts for formatting tokens such as roles and boundaries.
After making a request, inspect the returned usage rather than relying only on a pre-call estimate. Responses reports input_tokens, output_tokens and total_tokens; Chat Completions reports prompt_tokens, completion_tokens and total_tokens. Where the provider exposes additional categories, record cached input and reasoning usage separately. OpenAI says reasoning tokens count toward output usage and billing even when they are not visible in the answer.
3. Check how a third-party counter gets its number
Cross-provider calculators do not all count the same way. Some use local tokenizers for certain models, some call a provider’s counting endpoint, and some approximate where exact counting is unavailable. Read the counter’s method and confidence qualifications before treating its result as exact. For example, How Many Tokens? describes its methodology, while LLM Cost Check explains how it calculates its figures. These are descriptions of those services’ approaches, not guarantees that every model or request is counted exactly.
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For a basic request, calculate input-token cost plus output-token cost:
Estimated request cost = (input tokens × input rate) + (output tokens × output rate)
Use the rates for the chosen model and billing category. Add separate terms for cached input, cache writes, image, audio or video processing, tools, or other provider-specific billing units when they apply. A calculator that assumes an output length cannot know the actual generated-token count in advance, so label that length as an assumption.
For a monthly projection, multiply the estimated cost per request by expected request volume and state the assumed task mix and output lengths. Use current official provider prices when building a budget: prices can change, and independent calculators may manually transcribe rates or show figures that have become stale. Measure a sample of real calls and check billing records before committing to a forecast.
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Account for multi-turn history
In a stateless API workflow, the application generally has to send conversation history again on later turns. That means a later request may include previous user and assistant messages as input, even though the visible new message is short. Count the input actually sent on each turn and sum the turns; do not assume every exchange has the same token cost. The result depends on how the application manages history and on provider behavior.
Include costs an estimate may leave out
Some calculators exclude modalities, tools, embeddings, fine-tuning, failed requests or retries. Those exclusions mean the displayed total may not match an invoice. Check the provider’s billing records for any of these items used by your workload, and keep failed calls and retries in the measurement set if they occur in production.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare cost per useful result, not just token rates
Run the same representative task set on each candidate model. Record actual input and output usage, applicable cache rates, current invoice rates, task success or quality, retries and any history or modality costs. Then compare what it costs to produce an accepted result or complete a workflow. The lower per-token price is only one input; a model that consumes more tokens or requires more attempts can have a higher cost per useful result.
- Use the same tasks, request structure and success criteria across models.
- Keep measured usage distinct from assumptions about future volume or output length.
- Record the token-counting method and whether it is exact, endpoint-based or approximate.
- Recheck official rates and billing categories when prices or the workload change.
What the 2.3× claim establishes—and what it does not
No identifiable published table, underlying dataset or reproducible comparison was found to establish that a table was off by 2.3×. Without the original table and its assumptions, the figure cannot be checked: the models, task, token definition, pricing date and arithmetic are unknown. It is therefore not evidence that AI token estimates are generally wrong by that amount. A valid 2.3× comparison would need to disclose those inputs and show how the measured counts and rates produce the result.
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