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For models that support it, the Anannas API can return Reasoning Tokens, also known as thinking tokens. Anannas normalizes the different ways of customizing the amount of reasoning tokens that the model will use, providing a unified interface across different providers. Reasoning tokens provide a transparent look into the reasoning steps taken by a model. Reasoning tokens are considered output tokens and charged accordingly. Reasoning tokens are included in the response by default if the model decides to output them. Reasoning tokens will appear in the reasoning field of each message, unless you decide to exclude them.
Some reasoning models do not return their reasoning tokensWhile most models and providers make reasoning tokens available in the response, some (like the OpenAI o-series and Gemini Flash Thinking) do not.

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Controlling Reasoning Tokens

You can control reasoning tokens in your requests using the reasoning parameter: 
{
  "model": "your-model",
  "messages": [],
  "reasoning": {
    // One of the following (not both):
    "effort": "high", // Can be "high", "medium", or "low" (OpenAI-style)
    "max_tokens": 2000, // Specific token limit (Anthropic-style)

    // Optional: Default is false. All models support this.
    "exclude": false, // Set to true to exclude reasoning tokens from response

    // Or enable reasoning with the default parameters:
    "enabled": true // Default: inferred from `effort` or `max_tokens`
  }
}
The reasoning config object consolidates settings for controlling reasoning strength across different models. See the Note for each option below to see which models are supported and how other models will behave.

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Max Tokens for Reasoning

Currently supported by:
  • Gemini thinking models
  • Anthropic reasoning models (by using the reasoning.max_tokens parameter)
  • Some Alibaba Qwen thinking models (mapped to thinking_budget)
For Alibaba, support varies by model — please check the individual model descriptions to confirm whether reasoning.max_tokens (via thinking_budget) is available.
For models that support reasoning token allocation, you can control it like this:
  • "max_tokens": 2000 - Directly specifies the maximum number of tokens to use for reasoning
For models that only support reasoning.effort (see below), the max_tokens value will be used to determine the effort level.

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Reasoning Effort Level

Supported modelsCurrently supported by OpenAI reasoning models (o1 series, o3 series, GPT-5 series) and Grok models
  • "effort": "high" - Allocates a large portion of tokens for reasoning (approximately 80% of max_tokens)
  • "effort": "medium" - Allocates a moderate portion of tokens (approximately 50% of max_tokens)
  • "effort": "low" - Allocates a smaller portion of tokens (approximately 20% of max_tokens)
For models that only support reasoning.max_tokens, the effort level will be set based on the percentages above.

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Excluding Reasoning Tokens

If you want the model to use reasoning internally but not include it in the response:
  • "exclude": true - The model will still use reasoning, but it won’t be returned in the response
Reasoning tokens will appear in the reasoning field of each message.

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Enable Reasoning with Default Config

To enable reasoning with the default parameters:
  • "enabled": true - Enables reasoning at the “medium” effort level with no exclusions.

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Legacy Parameters

For backward compatibility, OpenRouter still supports the following legacy parameters:
  • include_reasoning: true - Equivalent to reasoning: {}
  • include_reasoning: false - Equivalent to reasoning: { exclude: true }
However, we recommend using the new unified reasoning parameter for better control and future compatibility.

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Examples

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Basic Usage with Reasoning Tokens

import requests
import json

url = "https://openrouter.ai/api/v1/chat/completions"
headers = {
    "Authorization": f"Bearer <OPENROUTER_API_KEY>",
    "Content-Type": "application/json"
}
payload = {
    "model": "openai/o3-mini",
    "messages": [
        {"role": "user", "content": "How would you build the world's tallest skyscraper?"}
    ],
    "reasoning": {
        "effort": "high"  # Use high reasoning effort
    }
}

response = requests.post(url, headers=headers, data=json.dumps(payload))
print(response.json()['choices'][0]['message']['reasoning'])

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Using Max Tokens for Reasoning

For models that support direct token allocation (like Anthropic models), you can specify the exact number of tokens to use for reasoning: 
import requests
import json

url = "https://openrouter.ai/api/v1/chat/completions"
headers = {
    "Authorization": f"Bearer <OPENROUTER_API_KEY>",
    "Content-Type": "application/json"
}
payload = {
    "model": "anthropic/claude-3.7-sonnet",
    "messages": [
        {"role": "user", "content": "What's the most efficient algorithm for sorting a large dataset?"}
    ],
    "reasoning": {
        "max_tokens": 2000  # Allocate 2000 tokens (or approximate effort) for reasoning
    }
}

response = requests.post(url, headers=headers, data=json.dumps(payload))
print(response.json()['choices'][0]['message']['reasoning'])
print(response.json()['choices'][0]['message']['content'])

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Excluding Reasoning Tokens from Response

If you want the model to use reasoning internally but not include it in the response: 
import requests
import json

url = "https://openrouter.ai/api/v1/chat/completions"
headers = {
    "Authorization": f"Bearer <OPENROUTER_API_KEY>",
    "Content-Type": "application/json"
}
payload = {
    "model": "deepseek/deepseek-r1",
    "messages": [
        {"role": "user", "content": "Explain quantum computing in simple terms."}
    ],
    "reasoning": {
        "effort": "high",
        "exclude": true  # Use reasoning but don't include it in the response
    }
}

response = requests.post(url, headers=headers, data=json.dumps(payload))
# No reasoning field in the response
print(response.json()['choices'][0]['message']['content'])

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Advanced Usage: Reasoning Chain-of-Thought

This example shows how to use reasoning tokens in a more complex workflow. It injects one model’s reasoning into another model to improve its response quality: 
import requests
import json

question = "Which is bigger: 9.11 or 9.9?"

url = "https://openrouter.ai/api/v1/chat/completions"
headers = {
    "Authorization": f"Bearer <OPENROUTER_API_KEY>",
    "Content-Type": "application/json"
}

def do_req(model, content, reasoning_config=None):
    payload = {
        "model": model,
        "messages": [
            {"role": "user", "content": content}
        ],
        "stop": "</think>"
    }

    return requests.post(url, headers=headers, data=json.dumps(payload))

# Get reasoning from a capable model
content = f"{question} Please think this through, but don't output an answer"
reasoning_response = do_req("deepseek/deepseek-r1", content)
reasoning = reasoning_response.json()['choices'][0]['message']['reasoning']

# Let's test! Here's the naive response:
simple_response = do_req("openai/gpt-4o-mini", question)
print(simple_response.json()['choices'][0]['message']['content'])

# Here's the response with the reasoning token injected:
content = f"{question}. Here is some context to help you: {reasoning}"
smart_response = do_req("openai/gpt-4o-mini", content)
print(smart_response.json()['choices'][0]['message']['content'])

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Provider-Specific Reasoning Implementation

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Anthropic Models with Reasoning Tokens

The latest Claude models, such as anthropic/claude-3.7-sonnet, support working with and returning reasoning tokens. You can enable reasoning on Anthropic models only using the unified reasoningparameter with either effort or max_tokens. Note: The :thinking variant is no longer supported for Anthropic models. Use the reasoning parameter instead.

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Reasoning Max Tokens for Anthropic Models

When using Anthropic models with reasoning:
  • When using the reasoning.max_tokens parameter, that value is used directly with a minimum of 1024 tokens.
  • When using the reasoning.effort parameter, the budget_tokens are calculated based on the max_tokens value.
The reasoning token allocation is capped at 32,000 tokens maximum and 1024 tokens minimum. The formula for calculating the budget_tokens is: budget_tokens = max(min(max_tokens * {effort_ratio}, 32000), 1024) effort_ratio is 0.8 for high effort, 0.5 for medium effort, and 0.2 for low effort. Important: max_tokens must be strictly higher than the reasoning budget to ensure there are tokens available for the final response after thinking.
Token Usage and BillingPlease note that reasoning tokens are counted as output tokens for billing purposes. Using reasoning tokens will increase your token usage but can significantly improve the quality of model responses.

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Examples with Anthropic Models

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Example 1: Streaming mode with reasoning tokens

from openai import OpenAI

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key="<OPENROUTER_API_KEY>",
)

def chat_completion_with_reasoning(messages):
    response = client.chat.completions.create(
        model="anthropic/claude-3.7-sonnet",
        messages=messages,
        max_tokens=10000,
        reasoning={
            "max_tokens": 8000  # Directly specify reasoning token budget
        },
        stream=True
    )
    return response

for chunk in chat_completion_with_reasoning([
    {"role": "user", "content": "What's bigger, 9.9 or 9.11?"}
]):
    if hasattr(chunk.choices[0].delta, 'reasoning') and chunk.choices[0].delta.reasoning:
        print(f"REASONING: {chunk.choices[0].delta.reasoning}")
    elif chunk.choices[0].delta.content:
        print(f"CONTENT: {chunk.choices[0].delta.content}")

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Preserving Reasoning Blocks

Model SupportThe reasoning_details are currently returned by all OpenAI reasoning models (o1 series, o3 series, GPT-5 series) and all Anthropic reasoning models (Claude 3.7, Claude 4, and Claude 4.1 series).
The reasoning_details functionality works identically across all supported reasoning models. You can easily switch between OpenAI reasoning models (like openai/gpt-5-mini) and Anthropic reasoning models (like anthropic/claude-sonnet-4) without changing your code structure. If you want to pass reasoning back in context, you must pass reasoning blocks back to the API. This is useful for maintaining the model’s reasoning flow and conversation integrity. Preserving reasoning blocks is useful specifically for tool calling. When models like Claude invoke tools, it is pausing its construction of a response to await external information. When tool results are returned, the model will continue building that existing response. This necessitates preserving reasoning blocks during tool use, for a couple of reasons: Reasoning continuity: The reasoning blocks capture the model’s step-by-step reasoning that led to tool requests. When you post tool results, including the original reasoning ensures the model can continue its reasoning from where it left off. Context maintenance: While tool results appear as user messages in the API structure, they’re part of a continuous reasoning flow. Preserving reasoning blocks maintains this conceptual flow across multiple API calls.
Important for Reasoning ModelsWhen providing reasoning_details blocks, the entire sequence of consecutive reasoning blocks must match the outputs generated by the model during the original request; you cannot rearrange or modify the sequence of these blocks.

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Responses API Shape

When reasoning models generate responses, the reasoning information is structured in a standardized format through the reasoning_details array. This section documents the API response structure for reasoning details in both streaming and non-streaming responses, based on the schema definitions in the llm-interfaces package.

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reasoning_details Array Structure

The reasoning_details field contains an array of reasoning detail objects. Each object in the array represents a specific piece of reasoning information and follows one of three possible types. The location of this array differs between streaming and non-streaming responses:
  • Non-streaming responses: reasoning_details appears in choices[].message.reasoning_details
  • Streaming responses: reasoning_details appears in choices[].delta.reasoning_details for each chunk

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Common Fields

All reasoning detail objects share these common fields:
  • id (string | null): Unique identifier for the reasoning detail
  • format (string): The format of the reasoning detail, with possible values:
    • "unknown" - Format is not specified
    • "openai-responses-v1" - OpenAI responses format version 1
    • "anthropic-claude-v1" - Anthropic Claude format version 1 (default)
  • index (number, optional): Sequential index of the reasoning detail

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Reasoning Detail Types

1. Summary Type (reasoning.summary) Contains a high-level summary of the reasoning process: 
{
  "type": "reasoning.summary",
  "summary": "The model analyzed the problem by first identifying key constraints, then evaluating possible solutions...",
  "id": "reasoning-summary-1",
  "format": "anthropic-claude-v1",
  "index": 0
}
2. Encrypted Type (reasoning.encrypted) Contains encrypted reasoning data that may be redacted or protected: 
{  
	"type": "reasoning.encrypted",  
	"data": "eyJlbmNyeXB0ZWQiOiJ0cnVlIiwiY29udGVudCI6IltSRURBQ1RFRF0ifQ==",  
	"id": "reasoning-encrypted-1",  
	"format": "anthropic-claude-v1",  
	"index": 1
}
3. Text Type (reasoning.text) Contains raw text reasoning with optional signature verification: 
{  
	"type": "reasoning.text",  
	"text": "Let me think through this step by step:\n1. First, I need to understand the user's question...",  
	"signature": "sha256:abc123def456...",  
	"id": "reasoning-text-1",  
	"format": "anthropic-claude-v1",  
	"index": 2
}

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Response Examples

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Non-Streaming Response

In non-streaming responses, reasoning_details appears in the message: 
// add code here{
  "choices": [
    {
      "message": {
        "role": "assistant",
        "content": "Based on my analysis, I recommend the following approach...",
        "reasoning_details": [
          {
            "type": "reasoning.summary",
            "summary": "Analyzed the problem by breaking it into components",
            "id": "reasoning-summary-1",
            "format": "anthropic-claude-v1",
            "index": 0
          },
          {
            "type": "reasoning.text",
            "text": "Let me work through this systematically:\n1. First consideration...\n2. Second consideration...",
            "signature": null,
            "id": "reasoning-text-1",
            "format": "anthropic-claude-v1",
            "index": 1
          }
        ]
      }
    }
  ]
}

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Streaming Response

In streaming responses, reasoning_details appears in delta chunks as the reasoning is generated: 
{
  "choices": [
    {
      "delta": {
        "reasoning_details": [
          {
            "type": "reasoning.text",
            "text": "Let me think about this step by step...",
            "signature": null,
            "id": "reasoning-text-1",
            "format": "anthropic-claude-v1",
            "index": 0
          }
        ]
      }
    }
  ]
}
Streaming Behavior Notes:
  • Each reasoning detail chunk is sent as it becomes available
  • The reasoning_details array in each chunk may contain one or more reasoning objects
  • For encrypted reasoning, the content may appear as [REDACTED] in streaming responses
  • The complete reasoning sequence is built by concatenating all chunks in order

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Example: Preserving Reasoning Blocks with OpenRouter and Claude

from openai import OpenAI

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key="<OPENROUTER_API_KEY>",
)

# First API call with tools
# Note: You can use 'openai/gpt-5-mini' instead of 'anthropic/claude-sonnet-4' - they're completely interchangeable
response = client.chat.completions.create(
    model="anthropic/claude-sonnet-4",
    messages=[
        {"role": "user", "content": "What's the weather like in Boston? Then recommend what to wear."}
    ],
    tools=[{
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string"}
                },
                "required": ["location"]
            }
        }
    }],
    reasoning={"max_tokens": 2000}
)

# Extract the assistant message with reasoning_details
message = response.choices[0].message

# Preserve the complete reasoning_details when passing back
messages = [
    {"role": "user", "content": "What's the weather like in Boston? Then recommend what to wear."},
    {
        "role": "assistant",
        "content": message.content,
        "tool_calls": message.tool_calls,
        "reasoning_details": message.reasoning_details  # Pass back unmodified
    },
    {
        "role": "tool",
        "tool_call_id": message.tool_calls[0].id,
        "content": '{"temperature": 45, "condition": "rainy", "humidity": 85}'
    }
]

# Second API call - Claude continues reasoning from where it left off
response2 = client.chat.completions.create(
    model="anthropic/claude-sonnet-4",
    messages=messages,  # Includes preserved thinking blocks
    tools=tools
)
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