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The same visible prompt can produce different token counts—and different bills—on different AI models. Each model uses its own tokenizer, and the complete API request may include structure or non-text inputs that are absent from the text you copied. To compare costs, count the full request with each target model’s official tool, then apply that provider’s current rates to each billed category.
Why does the same prompt use different token counts on different models?
A token is a unit of text or other input that a model processes. Tokenizers split content into tokens, but the rules for splitting it vary by model. As a result, identical words can yield different token counts; a count from one model’s tokenizer should not be treated as the count for another. OpenAI’s token guidance and Anthropic’s counting documentation describe model-specific counting.
The visible prompt may not represent the entire input sent to an API. Message structure, tools, schemas, images, and files can affect the count. Anthropic also notes that its count may include tokens inserted automatically for system optimizations. A plain-text count can therefore differ from the count for the complete request.
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Count the same complete request you intend to submit, not just a text excerpt. That means accounting for the request’s structure and any modalities, tools, or structured data that apply. Google’s Gemini token guide explains that image, video, and audio inputs are tokenized too.
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- Messages and instructions: Include the actual messages and their roles as represented in the API request.
- Tools and schemas: Include tool definitions or structured output schemas when they are part of the request.
- Files and media: Include the images, audio, video, or other supported content you will submit; a copied text description is not a substitute for counting those inputs.
- Provider-added material: Use the provider’s request counter, which may account for material not visible in your prompt text.
OpenAI recommends tiktoken for programmatic plain-text tokenization, but a local text count does not by itself establish how the complete API request will be counted. Check the target model’s documentation for limits and cost categories.
How do I count the same request for different models?
- Freeze the request. Keep the content and settings as consistent as possible, including any tools, schemas, files, or media.
- Count it with each target model’s official tool. Anthropic provides a
count_tokensendpoint that counts using the tokenizer of the model named in the request. Its guidance recommends comparing the same request against both the current and prospective models. - Record input counts separately. Do not transfer one model’s count to another, even when the visible prompt is identical.
- Estimate output separately. Decide how much generated output the task is likely to require and use the target model’s output rate.
- Check that both models can accept the request. Confirm their current context limits and intended settings; a count comparison is useful only if each request can be submitted as planned.
Google offers the Gemini CountTokens API for running its model tokenizer on input content. Use the provider’s model-specific counter for the request you intend to send, especially when the request contains more than plain text.
Why isn’t a token count the same as a price?
A count tells you how many tokens are processed in a category; the provider’s pricing rules determine what those tokens cost. A useful estimate is:
(input tokens × applicable input rate) + (cached input tokens × applicable cached-input rate, if eligible) + (output tokens × applicable output rate)
Use the selected model’s current pricing page and include any other billable components its provider specifies. For example, OpenAI lists input, cached input, and output rates separately, and says built-in tool tokens are billed at the selected model’s rates. Do not assume other providers use the same categories or rules. See OpenAI API pricing.
- Input: Use the current rate and stated unit for the model you are comparing.
- Cached input: Apply a cached rate only if your actual workflow qualifies for that treatment.
- Output: Estimate expected generated tokens separately; output has its own listed rate on OpenAI’s pricing schedule.
- Other billable components: Account for applicable modalities, tools, or other categories under that provider’s rules.
Prices and model behavior can change. Check the live pricing page when estimating a budget rather than relying on an undated price figure.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Does a cheaper per-token rate mean a cheaper answer?
No. The total depends on both the applicable rates and the number of tokens used in each billed category. A model with a lower per-token rate may still cost more for a particular task if its request uses more tokens or it produces more output. The practical comparison is the estimated cost of the same complete request and expected response—not a headline rate in isolation.
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