MessagesContext management

Token counting

Token counting enables you to determine the number of tokens in a message before sending it to Claude, helping you make informed decisions about your prompts and usage. With token counting, you can

Proactively manage rate limits and costs
Make smart model routing decisions
Optimize prompts to be a specific length

This feature is eligible for Zero Data Retention (ZDR). When your organization has a ZDR arrangement, data sent through this feature is not stored after the API response is returned.

How to count message tokens

The token counting endpoint accepts the same structured list of inputs for creating a message, including support for system prompts, tools, images, and PDFs. The response contains the total number of input tokens.

The token count should be considered an estimate. In some cases, the actual number of input tokens used when creating a message may differ by a small amount.

Token counts may include tokens added automatically by Anthropic for system optimizations. You are not billed for system-added tokens. Billing reflects only your content.

Supported models

All active models support token counting.

Count tokens in basic messages

client = anthropic.Anthropic()

response = client.messages.count_tokens(
    model="claude-opus-4-7",
    system="You are a scientist",
    messages=[{"role": "user", "content": "Hello, Claude"}],
)

print(response.json())

Output

{ "input_tokens": 14 }

Count tokens in messages with tools

Server tool token counts only apply to the first sampling call.

client = anthropic.Anthropic()

response = client.messages.count_tokens(
    model="claude-opus-4-7",
    tools=[
        {
            "name": "get_weather",
            "description": "Get the current weather in a given location",
            "input_schema": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    }
                },
                "required": ["location"],
            },
        }
    ],
    messages=[{"role": "user", "content": "What's the weather like in San Francisco?"}],
)

print(response.json())

Output

{ "input_tokens": 403 }

Count tokens in messages with images

import base64
import httpx

image_url = "https://upload.wikimedia.org/wikipedia/commons/a/a7/Camponotus_flavomarginatus_ant.jpg"
image_media_type = "image/jpeg"
image_data = base64.standard_b64encode(httpx.get(image_url).content).decode("utf-8")

client = anthropic.Anthropic()

response = client.messages.count_tokens(
    model="claude-opus-4-7",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "source": {
                        "type": "base64",
                        "media_type": image_media_type,
                        "data": image_data,
                    },
                },
                {"type": "text", "text": "Describe this image"},
            ],
        }
    ],
)
print(response.json())

Output

{ "input_tokens": 1551 }

Count tokens in messages with extended thinking

See how the context window is calculated with extended thinking for more details

Thinking blocks from previous assistant turns are ignored and do not count toward your input tokens
Current assistant turn thinking does count toward your input tokens

client = anthropic.Anthropic()

response = client.messages.count_tokens(
    model="claude-sonnet-4-6",
    thinking={"type": "enabled", "budget_tokens": 16000},
    messages=[
        {
            "role": "user",
            "content": "Are there an infinite number of prime numbers such that n mod 4 == 3?",
        },
        {
            "role": "assistant",
            "content": [
                {
                    "type": "thinking",
                    "thinking": "This is a nice number theory question. Let's think about it step by step...",
                    "signature": "EuYBCkQYAiJAgCs1le6/Pol5Z4/JMomVOouGrWdhYNsH3ukzUECbB6iWrSQtsQuRHJID6lWV...",
                },
                {
                    "type": "text",
                    "text": "Yes, there are infinitely many prime numbers p such that p mod 4 = 3...",
                },
            ],
        },
        {"role": "user", "content": "Can you write a formal proof?"},
    ],
)

print(response.json())

Output

{ "input_tokens": 88 }

Count tokens in messages with PDFs

Token counting supports PDFs with the same limitations as the Messages API.

import base64
import anthropic

client = anthropic.Anthropic()

with open("document.pdf", "rb") as pdf_file:
    pdf_base64 = base64.standard_b64encode(pdf_file.read()).decode("utf-8")

response = client.messages.count_tokens(
    model="claude-opus-4-7",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "document",
                    "source": {
                        "type": "base64",
                        "media_type": "application/pdf",
                        "data": pdf_base64,
                    },
                },
                {"type": "text", "text": "Please summarize this document."},
            ],
        }
    ],
)

print(response.json())

Output

{ "input_tokens": 2188 }

Pricing and rate limits

Token counting is free to use but subject to requests per minute rate limits based on your usage tier. If you need higher limits, contact sales through the Claude Console.

Usage tier	Requests per minute (RPM)
1	100
2	2,000
3	4,000
4	8,000

Token counting and message creation have separate and independent rate limits. Usage of one does not count against the limits of the other.

FAQ

Was this page helpful?

MessagesContext management

Token counting

Token counting enables you to determine the number of tokens in a message before sending it to Claude, helping you make informed decisions about your prompts and usage. With token counting, you can

Proactively manage rate limits and costs
Make smart model routing decisions
Optimize prompts to be a specific length

This feature is eligible for Zero Data Retention (ZDR). When your organization has a ZDR arrangement, data sent through this feature is not stored after the API response is returned.

How to count message tokens

The token count should be considered an estimate. In some cases, the actual number of input tokens used when creating a message may differ by a small amount.

Token counts may include tokens added automatically by Anthropic for system optimizations. You are not billed for system-added tokens. Billing reflects only your content.

Supported models

All active models support token counting.

Count tokens in basic messages

client = anthropic.Anthropic()

response = client.messages.count_tokens(
    model="claude-opus-4-7",
    system="You are a scientist",
    messages=[{"role": "user", "content": "Hello, Claude"}],
)

print(response.json())

Output

{ "input_tokens": 14 }

Count tokens in messages with tools

Server tool token counts only apply to the first sampling call.

client = anthropic.Anthropic()

response = client.messages.count_tokens(
    model="claude-opus-4-7",
    tools=[
        {
            "name": "get_weather",
            "description": "Get the current weather in a given location",
            "input_schema": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    }
                },
                "required": ["location"],
            },
        }
    ],
    messages=[{"role": "user", "content": "What's the weather like in San Francisco?"}],
)

print(response.json())

Output

{ "input_tokens": 403 }

Count tokens in messages with images

import base64
import httpx

image_url = "https://upload.wikimedia.org/wikipedia/commons/a/a7/Camponotus_flavomarginatus_ant.jpg"
image_media_type = "image/jpeg"
image_data = base64.standard_b64encode(httpx.get(image_url).content).decode("utf-8")

client = anthropic.Anthropic()

response = client.messages.count_tokens(
    model="claude-opus-4-7",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "source": {
                        "type": "base64",
                        "media_type": image_media_type,
                        "data": image_data,
                    },
                },
                {"type": "text", "text": "Describe this image"},
            ],
        }
    ],
)
print(response.json())

Output

{ "input_tokens": 1551 }

Count tokens in messages with extended thinking

See how the context window is calculated with extended thinking for more details

Thinking blocks from previous assistant turns are ignored and do not count toward your input tokens
Current assistant turn thinking does count toward your input tokens

client = anthropic.Anthropic()

response = client.messages.count_tokens(
    model="claude-sonnet-4-6",
    thinking={"type": "enabled", "budget_tokens": 16000},
    messages=[
        {
            "role": "user",
            "content": "Are there an infinite number of prime numbers such that n mod 4 == 3?",
        },
        {
            "role": "assistant",
            "content": [
                {
                    "type": "thinking",
                    "thinking": "This is a nice number theory question. Let's think about it step by step...",
                    "signature": "EuYBCkQYAiJAgCs1le6/Pol5Z4/JMomVOouGrWdhYNsH3ukzUECbB6iWrSQtsQuRHJID6lWV...",
                },
                {
                    "type": "text",
                    "text": "Yes, there are infinitely many prime numbers p such that p mod 4 = 3...",
                },
            ],
        },
        {"role": "user", "content": "Can you write a formal proof?"},
    ],
)

print(response.json())

Output

{ "input_tokens": 88 }

Count tokens in messages with PDFs

Token counting supports PDFs with the same limitations as the Messages API.

import base64
import anthropic

client = anthropic.Anthropic()

with open("document.pdf", "rb") as pdf_file:
    pdf_base64 = base64.standard_b64encode(pdf_file.read()).decode("utf-8")

response = client.messages.count_tokens(
    model="claude-opus-4-7",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "document",
                    "source": {
                        "type": "base64",
                        "media_type": "application/pdf",
                        "data": pdf_base64,
                    },
                },
                {"type": "text", "text": "Please summarize this document."},
            ],
        }
    ],
)

print(response.json())

Output

{ "input_tokens": 2188 }

Pricing and rate limits

Token counting is free to use but subject to requests per minute rate limits based on your usage tier. If you need higher limits, contact sales through the Claude Console.

Usage tier	Requests per minute (RPM)
1	100
2	2,000
3	4,000
4	8,000

Token counting and message creation have separate and independent rate limits. Usage of one does not count against the limits of the other.

FAQ

Was this page helpful?

How to count message tokens

Supported models

Count tokens in basic messages

Count tokens in messages with tools

Count tokens in messages with images

Count tokens in messages with extended thinking

Count tokens in messages with PDFs

Pricing and rate limits

FAQ

Does token counting use prompt caching?

How to count message tokens

Supported models

Count tokens in basic messages

Count tokens in messages with tools

Count tokens in messages with images

Count tokens in messages with extended thinking

Count tokens in messages with PDFs

Pricing and rate limits

FAQ

Does token counting use prompt caching?