Anthropic

统计 token

统计一段请求消息会消耗多少输入 token。

POST /anthropic/v1/messages/count_tokens◐ 部分支持

Anthropic 官方文档 ↗

请求体

  • 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.

    必填并校验存在性,用于选择原生线路和本地分词器。

  • messagesarray必填◐ 部分支持

    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.

    原生路径保留消息;本地使用 o200k 或按模型族校准的估算,图片按已知模型规则估算,未知尺寸使用近似额度;不下载图片 URL。

    ▸ 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◐ 部分支持

    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).

    原生透传;本地计入估算。

  • toolsarray可选◐ 部分支持

    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.

    原生透传;本地计入定义与包装的估算。

  • tool_choiceobject可选◐ 部分支持

    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.

    原生透传;本地计入估算。

    ▸ 展开子字段(3)
    • 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◐ 部分支持

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

    原生路径保留消息内标记;计数不能预测缓存命中或计费。

    ▸ 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◐ 部分支持

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

    原生路径透传;本地不承诺其附加模板的精确计数。

    ▸ 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◐ 部分支持

    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.

    原生路径透传;本地不承诺思考模板精确计数。

    ▸ 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`.

返回

200

按模型族校准的估算值(包括中文和工具定义),响应头为 x-praxis-token-count: estimated;不能用作精确用量或计费依据。

错误按 Anthropic 格式返回,参见 错误码

响应

HTTP 200

Token count

application/json

名称类型说明
input_tokensnumberThe total number of tokens across the provided list of messages, system prompt, and tools.