> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mixroute.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Count Tokens (Claude)

> Calculate Claude message token count for cost estimation before sending requests

## Introduction

Calculate the token count for Claude messages, used for cost estimation before sending requests. This endpoint **does not consume quota**, only performs local calculation.

## Authentication

Bearer Token, e.g., `Bearer sk-xxxxxxxxxx`

## Request Parameters

<ParamField body="model" type="string" required>
  Claude model identifier, supported models include:

  * `claude-opus-4-8`
  * `claude-sonnet-5`
  * `claude-haiku-4-5`
  * Other Claude series models
</ParamField>

<ParamField body="messages" type="array" required>
  Conversation messages list, each element contains `role` (user/assistant) and `content`. `content` can be a string or media content array.

  Supported content types:

  * Plain text messages
  * Multimodal messages (with images)
  * Tool call results
</ParamField>

<ParamField body="system" type="string">
  System prompt (optional), can be a string or media content array. Used to set model behavior and role.
</ParamField>

<ParamField body="tools" type="array">
  Tool definition list (optional), used to calculate tool call related token count.
</ParamField>

## Response Parameters

<ResponseField name="input_tokens" type="integer">
  Total token count of input messages, including:

  * All messages token count
  * System prompt token count
  * Tools definition token count (if any)
</ResponseField>

## Basic Examples

<Tabs>
  <Tab title="Simple Text Message">
    ```bash theme={null}
    curl -X POST "https://api.mixroute.ai/v1/messages/count_tokens" \
      -H "Content-Type: application/json" \
      -H "Authorization: Bearer sk-xxxxxxxxxx" \
      -d '{
        "model": "claude-opus-4-8",
        "messages": [
          {
            "role": "user",
            "content": "Hello, how are you?"
          }
        ]
      }'
    ```
  </Tab>

  <Tab title="With System Prompt">
    ```bash theme={null}
    curl -X POST "https://api.mixroute.ai/v1/messages/count_tokens" \
      -H "Content-Type: application/json" \
      -H "Authorization: Bearer sk-xxxxxxxxxx" \
      -d '{
        "model": "claude-opus-4-8",
        "system": "You are a helpful AI assistant.",
        "messages": [
          {
            "role": "user",
            "content": "What is artificial intelligence?"
          }
        ]
      }'
    ```
  </Tab>

  <Tab title="Multi-turn Conversation">
    ```bash theme={null}
    curl -X POST "https://api.mixroute.ai/v1/messages/count_tokens" \
      -H "Content-Type: application/json" \
      -H "Authorization: Bearer sk-xxxxxxxxxx" \
      -d '{
        "model": "claude-opus-4-8",
        "messages": [
          {
            "role": "user",
            "content": "Hello"
          },
          {
            "role": "assistant",
            "content": "Hello! How can I help you?"
          },
          {
            "role": "user",
            "content": "Tell me about the history of artificial intelligence"
          }
        ]
      }'
    ```
  </Tab>
</Tabs>

## Python Example

```python theme={null}
from anthropic import Anthropic

client = Anthropic(
    api_key="sk-xxxxxxxxxx",
    base_url="https://api.mixroute.ai"
)

# Count tokens
response = client.messages.count_tokens(
    model="claude-opus-4-8",
    system="You are a helpful assistant.",
    messages=[
        {"role": "user", "content": "Hello, Claude!"}
    ]
)

print(f"Input tokens: {response.input_tokens}")
```

## Response Example

```json theme={null}
{
  "input_tokens": 14
}
```

## Advanced Use Cases

<Tabs>
  <Tab title="With Tool Definitions">
    ```bash theme={null}
    curl -X POST "https://api.mixroute.ai/v1/messages/count_tokens" \
      -H "Content-Type: application/json" \
      -H "Authorization: Bearer sk-xxxxxxxxxx" \
      -d '{
        "model": "claude-opus-4-8",
        "messages": [
          {
            "role": "user",
            "content": "What is the weather in San Francisco?"
          }
        ],
        "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"]
            }
          }
        ]
      }'
    ```
  </Tab>

  <Tab title="Multimodal Content">
    ```bash theme={null}
    curl -X POST "https://api.mixroute.ai/v1/messages/count_tokens" \
      -H "Content-Type: application/json" \
      -H "Authorization: Bearer sk-xxxxxxxxxx" \
      -d '{
        "model": "claude-opus-4-8",
        "messages": [
          {
            "role": "user",
            "content": [
              {
                "type": "text",
                "text": "What is in this image?"
              },
              {
                "type": "image",
                "source": {
                  "type": "url",
                  "url": "https://example.com/image.jpg"
                }
              }
            ]
          }
        ]
      }'
    ```
  </Tab>
</Tabs>

## Use Cases

### 1. Cost Estimation

Calculate token count before sending bulk requests to estimate costs:

```python theme={null}
# Batch cost calculation
messages_batch = [...]  # Batch messages
total_tokens = 0

for messages in messages_batch:
    response = client.messages.count_tokens(
        model="claude-opus-4-8",
        messages=messages
    )
    total_tokens += response.input_tokens

# Calculate total cost based on pricing
cost = total_tokens * price_per_token
print(f"Estimated cost: ${cost:.4f}")
```

### 2. Context Window Management

Check if messages exceed the model's context window limit:

```python theme={null}
MAX_CONTEXT_WINDOW = 1000000  # Claude Sonnet 5 context window

response = client.messages.count_tokens(
    model="claude-opus-4-8",
    messages=long_conversation
)

if response.input_tokens > MAX_CONTEXT_WINDOW:
    print(f"Warning: Message token count ({response.input_tokens}) exceeds context window limit")
    # Execute message truncation or summarization
```

### 3. Prompt Optimization

Compare token consumption of different prompts:

```python theme={null}
prompts = [
    "Concise prompt...",
    "Detailed prompt...",
    "Very detailed prompt..."
]

for prompt in prompts:
    response = client.messages.count_tokens(
        model="claude-opus-4-8",
        system=prompt,
        messages=[{"role": "user", "content": "Test"}]
    )
    print(f"{len(prompt)} characters -> {response.input_tokens} tokens")
```

## Notes

* This endpoint does not make actual AI requests, does not consume quota
* Does not include output-related parameters like `max_tokens`, only calculates input token count
* Image tokens use fixed estimates (approx 1000 tokens), actual may vary based on resolution

## Error Handling

### Missing Required Parameters

```json theme={null}
{
  "type": "error",
  "error": {
    "type": "invalid_request_error",
    "message": "Key: 'ClaudeCountTokensRequest.Model' Error:Field validation for 'Model' failed on the 'required' tag"
  }
}
```

### Invalid API Key

```json theme={null}
{
  "error": {
    "message": "Invalid token",
    "type": "invalid_request_error"
  }
}
```

## Related Resources

* Pricing - Learn about token billing standards
* [Model List](https://console.mixroute.ai/models) - View supported Claude models
* [Create Message Request (Claude)](/en/api-reference/endpoint/messages) - Send actual Claude requests

<RequestExample>
  ```bash cURL theme={null}
  curl --request POST \
    --url https://api.mixroute.ai/v1/messages/count_tokens \
    --header 'Authorization: Bearer sk-xxxxxxxxxx' \
    --header 'Content-Type: application/json' \
    --data '{
      "model": "claude-opus-4-8",
      "messages": [
        {"role": "user", "content": "Hello, how are you?"}
      ]
    }'
  ```

  ```python Python theme={null}
  from anthropic import Anthropic

  client = Anthropic(
      api_key="sk-xxxxxxxxxx",
      base_url="https://api.mixroute.ai"
  )

  response = client.messages.count_tokens(
      model="claude-opus-4-8",
      messages=[
          {"role": "user", "content": "Hello, how are you?"}
      ]
  )
  print(f"Input tokens: {response.input_tokens}")
  ```

  ```javascript JavaScript theme={null}
  const response = await fetch('https://api.mixroute.ai/v1/messages/count_tokens', {
    method: 'POST',
    headers: {
      'Authorization': 'Bearer sk-xxxxxxxxxx',
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      model: 'claude-opus-4-8',
      messages: [
        { role: 'user', content: 'Hello, how are you?' }
      ]
    })
  });
  const data = await response.json();
  console.log(data);
  ```

  ```php PHP theme={null}
  <?php
  $client = new GuzzleHttp\Client();
  $response = $client->post('https://api.mixroute.ai/v1/messages/count_tokens', [
      'headers' => [
          'Authorization' => 'Bearer sk-xxxxxxxxxx',
          'Content-Type' => 'application/json',
      ],
      'json' => [
          'model' => 'claude-opus-4-8',
          'messages' => [
              ['role' => 'user', 'content' => 'Hello, how are you?']
          ]
      ]
  ]);
  echo $response->getBody();
  ```

  ```go Go theme={null}
  package main

  import (
      "bytes"
      "encoding/json"
      "net/http"
  )

  func main() {
      payload := map[string]interface{}{
          "model": "claude-opus-4-8",
          "messages": []map[string]string{
              {"role": "user", "content": "Hello, how are you?"},
          },
      }
      body, _ := json.Marshal(payload)
      req, _ := http.NewRequest("POST", "https://api.mixroute.ai/v1/messages/count_tokens", bytes.NewBuffer(body))
      req.Header.Set("Authorization", "Bearer sk-xxxxxxxxxx")
      req.Header.Set("Content-Type", "application/json")
      http.DefaultClient.Do(req)
  }
  ```

  ```java Java theme={null}
  import java.net.http.*;
  import java.net.URI;

  HttpClient client = HttpClient.newHttpClient();
  String json = """
      {
        "model": "claude-opus-4-8",
        "messages": [
          {"role": "user", "content": "Hello, how are you?"}
        ]
      }
      """;
  HttpRequest request = HttpRequest.newBuilder()
      .uri(URI.create("https://api.mixroute.ai/v1/messages/count_tokens"))
      .header("Authorization", "Bearer sk-xxxxxxxxxx")
      .header("Content-Type", "application/json")
      .POST(HttpRequest.BodyPublishers.ofString(json))
      .build();
  HttpResponse<String> response = client.send(request, HttpResponse.BodyHandlers.ofString());
  System.out.println(response.body());
  ```

  ```ruby Ruby theme={null}
  require 'net/http'
  require 'json'

  uri = URI('https://api.mixroute.ai/v1/messages/count_tokens')
  http = Net::HTTP.new(uri.host, uri.port)
  http.use_ssl = true

  request = Net::HTTP::Post.new(uri)
  request['Authorization'] = 'Bearer sk-xxxxxxxxxx'
  request['Content-Type'] = 'application/json'
  request.body = {
    model: 'claude-opus-4-8',
    messages: [
      { role: 'user', content: 'Hello, how are you?' }
    ]
  }.to_json

  response = http.request(request)
  puts response.body
  ```
</RequestExample>

<ResponseExample>
  ```json Response theme={null}
  {
    "input_tokens": 14
  }
  ```
</ResponseExample>