6.8 KiB
Count Tokens
$ ant messages count-tokens
post /v1/messages/count_tokens
Count the number of tokens in a Message.
The Token Count API can be used to count the number of tokens in a Message, including tools, images, and documents, without creating it.
Learn more about token counting in our user guide
Parameters
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--message: array of MessageParamInput messages.
Our models are trained to operate on alternating
userandassistantconversational turns. When creating a newMessage, you specify the prior conversational turns with themessagesparameter, and the model then generates the nextMessagein the conversation. Consecutiveuserorassistantturns in your request will be combined into a single turn.Each input message must be an object with a
roleandcontent. You can specify a singleuser-role message, or you can include multipleuserandassistantmessages.If the final message uses the
assistantrole, 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
usermessage:[{"role": "user", "content": "Hello, Claude"}]Example with multiple conversational turns:
[ {"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:
[ {"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
contentmay be either a singlestringor an array of content blocks, where each block has a specifictype. Using astringforcontentis shorthand for an array of one content block of type"text". The following input messages are equivalent:{"role": "user", "content": "Hello, Claude"}{"role": "user", "content": [{"type": "text", "text": "Hello, Claude"}]}See input examples.
Note that if you want to include a system prompt, you can use the top-level
systemparameter — there is no"system"role for input messages in the Messages API.There is a limit of 100,000 messages in a single request.
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--model: "claude-opus-4-7" or "claude-mythos-preview" or "claude-opus-4-6" or 14 more or stringThe model that will complete your prompt.
See models for additional details and options.
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--cache-control: optional object { type, ttl }Top-level cache control automatically applies a cache_control marker to the last cacheable block in the request.
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--output-config: optional object { effort, format }Configuration options for the model's output, such as the output format.
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--system: optional string or array of TextBlockParamSystem 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.
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--thinking: optional ThinkingConfigEnabled or ThinkingConfigDisabled or ThinkingConfigAdaptiveConfiguration for enabling Claude's extended thinking.
When enabled, responses include
thinkingcontent blocks showing Claude's thinking process before the final answer. Requires a minimum budget of 1,024 tokens and counts towards yourmax_tokenslimit.See extended thinking for details.
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--tool-choice: optional ToolChoiceAuto or ToolChoiceAny or ToolChoiceTool or ToolChoiceNoneHow 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.
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--tool: optional array of MessageCountTokensToolDefinitions of tools that the model may use.
If you include
toolsin your API request, the model may returntool_usecontent 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 usingtool_resultcontent blocks.There are two types of tools: client tools and server tools. The behavior described below applies to client tools. For server tools, see their individual documentation as each has its own behavior (e.g., the 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 for the toolinputshape that the model will produce intool_useoutput content blocks.
For example, if you defined
toolsas:[ { "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_usecontent blocks in the response like this:[ { "type": "tool_use", "id": "toolu_01D7FLrfh4GYq7yT1ULFeyMV", "name": "get_stock_price", "input": { "ticker": "^GSPC" } } ]You might then run your
get_stock_pricetool with{"ticker": "^GSPC"}as an input, and return the following back to the model in a subsequentusermessage:[ { "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 for more details.
Returns
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message_tokens_count: object { input_tokens }-
input_tokens: numberThe total number of tokens across the provided list of messages, system prompt, and tools.
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Example
ant messages count-tokens \
--api-key my-anthropic-api-key \
--message '{content: [{text: x, type: text}], role: user}' \
--model claude-opus-4-6