Responses API Reasoning - Advanced AI Reasoning

The Responses API supports advanced reasoning capabilities, allowing models to show their internal reasoning process with configurable effort levels.

Reasoning Configuration

Configure reasoning behavior using the reasoning parameter:

const response = await fetch('https://openrouter.ai/api/v1/responses', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_OPENROUTER_API_KEY',
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    model: 'openai/o4-mini',
    input: 'What is the meaning of life?',
    reasoning: {
      effort: 'high'
    },
    max_output_tokens: 9000,
  }),
});

const result = await response.json();
console.log(result);

import requests

response = requests.post(
    'https://openrouter.ai/api/v1/responses',
    headers={
        'Authorization': 'Bearer YOUR_OPENROUTER_API_KEY',
        'Content-Type': 'application/json',
    },
    json={
        'model': 'openai/o4-mini',
        'input': 'What is the meaning of life?',
        'reasoning': {
            'effort': 'high'
        },
        'max_output_tokens': 9000,
    }
)

result = response.json()
print(result)

curl -X POST https://openrouter.ai/api/v1/responses \
  -H "Authorization: Bearer YOUR_OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/o4-mini",
    "input": "What is the meaning of life?",
    "reasoning": {
      "effort": "high"
    },
    "max_output_tokens": 9000
  }'

Reasoning Effort Levels

The effort parameter controls how much computational effort the model puts into reasoning:

Effort Level	Description
`minimal`	Basic reasoning with minimal computational effort
`low`	Light reasoning for simple problems
`medium`	Balanced reasoning for moderate complexity
`high`	Deep reasoning for complex problems

To change effort partway through a conversation without invalidating the prompt cache, add a configuration_update input item before the user message it should apply to. The item is interchangeable with the Chat Completions and Anthropic Messages API forms. See Changing Effort Mid-Conversation for the request shape, placement rules, and supported models.

Complex Reasoning Example

For complex mathematical or logical problems:

const response = await fetch('https://openrouter.ai/api/v1/responses', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_OPENROUTER_API_KEY',
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    model: 'openai/o4-mini',
    input: [
      {
        type: 'message',
        role: 'user',
        content: [
          {
            type: 'input_text',
            text: 'Was 1995 30 years ago? Please show your reasoning.',
          },
        ],
      },
    ],
    reasoning: {
      effort: 'high'
    },
    max_output_tokens: 9000,
  }),
});

const result = await response.json();
console.log(result);

import requests

response = requests.post(
    'https://openrouter.ai/api/v1/responses',
    headers={
        'Authorization': 'Bearer YOUR_OPENROUTER_API_KEY',
        'Content-Type': 'application/json',
    },
    json={
        'model': 'openai/o4-mini',
        'input': [
            {
                'type': 'message',
                'role': 'user',
                'content': [
                    {
                        'type': 'input_text',
                        'text': 'Was 1995 30 years ago? Please show your reasoning.',
                    },
                ],
            },
        ],
        'reasoning': {
            'effort': 'high'
        },
        'max_output_tokens': 9000,
    }
)

result = response.json()
print(result)

Reasoning in Conversation Context

Include reasoning in multi-turn conversations:

const response = await fetch('https://openrouter.ai/api/v1/responses', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_OPENROUTER_API_KEY',
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    model: 'openai/o4-mini',
    input: [
      {
        type: 'message',
        role: 'user',
        content: [
          {
            type: 'input_text',
            text: 'What is your favorite color?',
          },
        ],
      },
      {
        type: 'message',
        role: 'assistant',
        id: 'msg_abc123',
        status: 'completed',
        content: [
          {
            type: 'output_text',
            text: "I don't have a favorite color.",
            annotations: []
          }
        ]
      },
      {
        type: 'message',
        role: 'user',
        content: [
          {
            type: 'input_text',
            text: 'How many Earths can fit on Mars?',
          },
        ],
      },
    ],
    reasoning: {
      effort: 'high'
    },
    max_output_tokens: 9000,
  }),
});

const result = await response.json();
console.log(result);

import requests

response = requests.post(
    'https://openrouter.ai/api/v1/responses',
    headers={
        'Authorization': 'Bearer YOUR_OPENROUTER_API_KEY',
        'Content-Type': 'application/json',
    },
    json={
        'model': 'openai/o4-mini',
        'input': [
            {
                'type': 'message',
                'role': 'user',
                'content': [
                    {
                        'type': 'input_text',
                        'text': 'What is your favorite color?',
                    },
                ],
            },
            {
                'type': 'message',
                'role': 'assistant',
                'id': 'msg_abc123',
                'status': 'completed',
                'content': [
                    {
                        'type': 'output_text',
                        'text': "I don't have a favorite color.",
                        'annotations': []
                    }
                ]
            },
            {
                'type': 'message',
                'role': 'user',
                'content': [
                    {
                        'type': 'input_text',
                        'text': 'How many Earths can fit on Mars?',
                    },
                ],
            },
        ],
        'reasoning': {
            'effort': 'high'
        },
        'max_output_tokens': 9000,
    }
)

result = response.json()
print(result)

Streaming Reasoning

Enable streaming to see reasoning develop in real-time:

const response = await fetch('https://openrouter.ai/api/v1/responses', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_OPENROUTER_API_KEY',
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    model: 'openai/o4-mini',
    input: 'Solve this step by step: If a train travels 60 mph for 2.5 hours, how far does it go?',
    reasoning: {
      effort: 'medium'
    },
    stream: true,
    max_output_tokens: 9000,
  }),
});

const reader = response.body?.getReader();
const decoder = new TextDecoder();

while (true) {
  const { done, value } = await reader.read();
  if (done) break;

  const chunk = decoder.decode(value);
  const lines = chunk.split('\n');

  for (const line of lines) {
    if (line.startsWith('data: ')) {
      const data = line.slice(6);
      if (data === '[DONE]') return;

      try {
        const parsed = JSON.parse(data);
        if (parsed.type === 'response.reasoning.delta') {
          console.log('Reasoning:', parsed.delta);
        }
      } catch (e) {
        // Skip invalid JSON
      }
    }
  }
}

import requests
import json

response = requests.post(
    'https://openrouter.ai/api/v1/responses',
    headers={
        'Authorization': 'Bearer YOUR_OPENROUTER_API_KEY',
        'Content-Type': 'application/json',
    },
    json={
        'model': 'openai/o4-mini',
        'input': 'Solve this step by step: If a train travels 60 mph for 2.5 hours, how far does it go?',
        'reasoning': {
            'effort': 'medium'
        },
        'stream': True,
        'max_output_tokens': 9000,
    },
    stream=True
)

for line in response.iter_lines():
    if line:
        line_str = line.decode('utf-8')
        if line_str.startswith('data: '):
            data = line_str[6:]
            if data == '[DONE]':
                break
            try:
                parsed = json.loads(data)
                if parsed.get('type') == 'response.reasoning.delta':
                    print(f"Reasoning: {parsed.get('delta', '')}")
            except json.JSONDecodeError:
                continue

Response with Reasoning

When reasoning is enabled, the response includes reasoning information:

{
  "id": "resp_1234567890",
  "object": "response",
  "created_at": 1234567890,
  "model": "openai/o4-mini",
  "output": [
    {
      "type": "reasoning",
      "id": "rs_abc123",
      "encrypted_content": "gAAAAABotI9-FK1PbhZhaZk4yMrZw3XDI1AWFaKb9T0NQq7LndK6zaRB...",
      "summary": [
        "First, I need to determine the current year",
        "Then calculate the difference from 1995",
        "Finally, compare that to 30 years"
      ]
    },
    {
      "type": "message",
      "id": "msg_xyz789",
      "status": "completed",
      "role": "assistant",
      "content": [
        {
          "type": "output_text",
          "text": "Yes. In 2025, 1995 was 30 years ago. In fact, as of today (Aug 31, 2025), it's exactly 30 years since Aug 31, 1995.",
          "annotations": []
        }
      ]
    }
  ],
  "usage": {
    "input_tokens": 15,
    "output_tokens": 85,
    "output_tokens_details": {
      "reasoning_tokens": 45
    },
    "total_tokens": 100
  },
  "status": "completed"
}

Best Practices

Choose appropriate effort levels: Use high for complex problems, low for simple tasks
Consider token usage: Reasoning increases token consumption
Use streaming: For long reasoning chains, streaming provides better user experience
Include context: Provide sufficient context for the model to reason effectively

Next Steps

Explore Tool Calling with reasoning
Learn about Web Search integration
Review Basic Usage fundamentals

API Guides

Versioning

API Reference

Reasoning

Reasoning Configuration

Reasoning Effort Levels

Complex Reasoning Example

Reasoning in Conversation Context

Streaming Reasoning

Response with Reasoning

Best Practices

Next Steps

​Reasoning Configuration

​Reasoning Effort Levels

​Complex Reasoning Example

​Reasoning in Conversation Context

​Streaming Reasoning

​Response with Reasoning

​Best Practices

​Next Steps

Reasoning Configuration

Reasoning Effort Levels

Complex Reasoning Example

Reasoning in Conversation Context

Streaming Reasoning

Response with Reasoning

Best Practices

Next Steps