Count tokens for a request

Request body

• modelstring必填

  Body param: The model that will complete your prompt. See [models](https://docs.anthropic.com/en/docs/models-overview) for additional details and options.

  Required and checked for existence; selects native lines and local tokenizers.

• messagesarray必填◐ Partially supported

  Body param: Input messages. Our models are trained to operate on alternating `user` and `assistant` conversational turns. When creating a new `Message`, you specify the prior conversational turns with the `messages` parameter, and the model then generates the next `Message` in the conversation. Consecutive `user` or `assistant` turns in your request will be combined into a single turn. Each input message must be an object with a `role` and `content`. You can specify a single `user`-role message, or you can include multiple `user` and `assistant` messages. If the final message uses the `assistant` role, the response content will continue immediately from the content in that message. This can be used to constrain part of the model's response. Example with a single `user` message: ```json [{ "role": "user", "content": "Hello, Claude" }] ``` Example with multiple conversational turns: ```json [ { "role": "user", "content": "Hello there." }, { "role": "assistant", "content": "Hi, I'm Claude. How can I help you?" }, { "role": "user", "content": "Can you explain LLMs in plain English?" } ] ``` Example with a partially-filled response from Claude: ```json [ { "role": "user", "content": "What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun" }, { "role": "assistant", "content": "The best answer is (" } ] ``` Each input message `content` may be either a single `string` or an array of content blocks, where each block has a specific `type`. Using a `string` for `content` is shorthand for an array of one content block of type `"text"`. The following input messages are equivalent: ```json { "role": "user", "content": "Hello, Claude" } ``` ```json { "role": "user", "content": [{ "type": "text", "text": "Hello, Claude" }] } ``` See [input examples](https://platform.claude.com/docs/en/build-with-claude/working-with-messages). Note that if you want to include a [system prompt](https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/claude-prompting-best-practices#give-claude-a-role), you can use the top-level `system` parameter — there is no `"system"` role for input messages in the Messages API. There is a limit of 100,000 messages in a single request.

  Native counting preserves messages. Local counting uses o200k or family-calibrated estimates; images use known model rules or approximate allowances for unknown dimensions. Image URLs are not downloaded.

  ▸ 展开子字段（2）

  • contentstring | array必填

    Message content: a plain string (shorthand for one text block) or an array of content blocks such as text, image, document, tool_use and tool_result.

  • rolestring必填

    Who produced the message. Use user for your turns and assistant to replay earlier model turns; system-role messages are accepted by the shape but system instructions normally go in the top-level system field.

• systemstring | array可选◐ Partially supported

  Body param: System prompt. A system prompt is a way of providing context and instructions to Claude, such as specifying a particular goal or role. See our [guide to system prompts](https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/claude-prompting-best-practices#give-claude-a-role).

  Forwarded natively; included in local estimates.

• toolsarray可选◐ Partially supported

  Body param: Definitions of tools that the model may use. If you include `tools` in your API request, the model may return `tool_use` content blocks that represent the model's use of those tools. You can then run those tools using the tool input generated by the model and then optionally return results back to the model using `tool_result` content blocks. There are two types of tools: **client tools** and **server tools**. The behavior described below applies to client tools. For [server tools](https://platform.claude.com/docs/en/agents-and-tools/tool-use/server-tools), see their individual documentation as each has its own behavior (e.g., the [web search tool](https://platform.claude.com/docs/en/agents-and-tools/tool-use/web-search-tool)). Each tool definition includes: - `name`: Name of the tool. - `description`: Optional, but strongly-recommended description of the tool. - `input_schema`: [JSON schema](https://json-schema.org/draft/2020-12) for the tool `input` shape that the model will produce in `tool_use` output content blocks. For example, if you defined `tools` as: ```json [ { "name": "get_stock_price", "description": "Get the current stock price for a given ticker symbol.", "input_schema": { "type": "object", "properties": { "ticker": { "type": "string", "description": "The stock ticker symbol, e.g. AAPL for Apple Inc." } }, "required": ["ticker"] } } ] ``` And then asked the model "What's the S&P 500 at today?", the model might produce `tool_use` content blocks in the response like this: ```json [ { "type": "tool_use", "id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", "name": "get_stock_price", "input": { "ticker": "^GSPC" } } ] ``` You might then run your `get_stock_price` tool with `{"ticker": "^GSPC"}` as an input, and return the following back to the model in a subsequent `user` message: ```json [ { "type": "tool_result", "tool_use_id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", "content": "259.75 USD" } ] ``` Tools can be used for workflows that include running client-side tools and functions, or more generally whenever you want the model to produce a particular JSON structure of output. See our [guide](https://platform.claude.com/docs/en/agents-and-tools/tool-use/overview) for more details.

  Forwarded natively; local estimates include definitions and framing.

• tool_choiceobject可选◐ Partially supported

  Body param: How the model should use the provided tools. The model can use a specific tool, any available tool, decide by itself, or not use tools at all.

  Forwarded natively; included in local estimates.

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  • typestring必填

    Disable tool calls for this request.

    取值 none
  • disable_parallel_tool_useboolean可选

    Whether to disable parallel tool use. Defaults to `false`. If set to `true`, the model will output exactly one tool use.

  • namestring必填

    The name of the tool to use.

• cache_controlobject可选◐ Partially supported

  Body param: Top-level cache control automatically applies a cache_control marker to the last cacheable block in the request.

  Native counting preserves message cache markers; counts do not predict cache hits or billing.

  ▸ 展开子字段（2）

  • typestring必填

    Always `ephemeral`. Discriminator that marks this object as a CacheControlEphemeral in a `type`-tagged union.

    取值 ephemeral
  • ttlstring可选

    The time-to-live for the cache control breakpoint. This may be one the following values: - `5m`: 5 minutes - `1h`: 1 hour Defaults to `5m`. See [prompt caching pricing](https://platform.claude.com/docs/en/build-with-claude/prompt-caching) for details.

• output_configobject可选◐ Partially supported

  Body param: Configuration options for the model's output, such as the output format.

  Forwarded natively; additional template tokens are not exact locally.

  ▸ 展开子字段（2）

  • effortstring可选

    All possible effort levels.

  • formatobject可选

    A schema to specify Claude's output format in responses. See [structured outputs](https://platform.claude.com/docs/en/build-with-claude/structured-outputs)

• thinkingobject可选◐ Partially supported

  Body param: Configuration for enabling Claude's extended thinking. When enabled, responses include `thinking` content blocks showing Claude's thinking process before the final answer. Requires a minimum budget of 1,024 tokens and counts towards your `max_tokens` limit. See [extended thinking](https://platform.claude.com/docs/en/build-with-claude/extended-thinking) for details.

  Forwarded natively; local counting does not promise exact thinking-template accounting.

  ▸ 展开子字段（3）

  • budget_tokensnumber必填

    Determines how many tokens Claude can use for its internal reasoning process. Larger budgets can enable more thorough analysis for complex problems, improving response quality. Must be ≥1024 and less than `max_tokens`. See [extended thinking](https://platform.claude.com/docs/en/build-with-claude/extended-thinking) for details.

  • typestring必填

    Let the model choose its reasoning budget, subject to output_config.effort when supplied.

    取值 adaptive
  • displaystring可选

    Controls how thinking content appears in the response. When set to `summarized`, thinking is returned normally. When set to `omitted`, thinking content is redacted but a signature is returned for multi-turn continuity. Defaults to `summarized`.

Returns

200

Returns a model-family-calibrated estimate, including Chinese text and tool definitions, with the response header x-praxis-token-count: estimated. Do not use it as an exact usage or billing count.

Errors follow the Anthropic format — see error codes