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

# Reasoning

> The Responses API Beta suporta advanced reasoning capabilities, allowing the model to demonstrate its internal reasoning process when answering questions, and the level of reasoning effort can be configured via parameters.

## Reasoning Configuration

Você pode configure o reasoning behavior using the `reasoning` parameter:

```shellscript theme={null}
curl -X POST https://aihubmix.com/v1/responses \
  -H "Authorization: Bearer YOUR_AIHUBMIX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "glm-5",
    "input": "Plan a week-long trip to the US for me.",
    "reasoning": {
      "effort": "high"
    },
    "max_output_tokens": 5000
  }'
```

## Reasoning Intensity

The `effort` parameter controls how much computational resource the model invests in reasoning, essentially dictating the level of reasoning effort.

| Reasoning Level | Description                                                 |
| :-------------- | :---------------------------------------------------------- |
| minimal         | Basic reasoning with minimal computation                    |
| low             | Lightweight reasoning suitable for simple questions         |
| medium          | Balanced reasoning suitable for moderately complex problems |
| high            | Deep reasoning suitable for complex issues                  |

## Using Reasoning in Conversations

The reasoning feature can also be utilized in multi-turn dialogues:

```python theme={null}
import requests

url = "https://aihubmix.com/v1/responses"

headers = {
    "Authorization": "Bearer YOUR_AIHUBMIX_API_KEY",
    "Content-Type": "application/json",
}

data = {
    "model": "kimi-k2.5",
    "input": [
        {
            "type": "message",
            "role": "user",
            "content": [
                {
                    "type": "input_text",
                    "text": "What is your favorite animal?",
                }
            ],
        },
        {
            "type": "message",
            "role": "assistant",
            "id": "msg_123",
            "status": "completed",
            "content": [
                {
                    "type": "output_text",
                    "text": "I don't have a favorite animal.",
                    "annotations": []
                }
            ],
        },
        {
            "type": "message",
            "role": "user",
            "content": [
                {
                    "type": "input_text",
                    "text": "Why is the sky blue?",
                }
            ],
        },
    ],
    "reasoning": {
        "effort": "high"
    },
    "max_output_tokens": 5000,
}

response = requests.post(url, headers=headers, json=data)

print(response.status_code)
print(response.json())
```

## Responses Containing Reasoning Information

When reasoning is enabled, the API returns results that include reasoning data:

```json theme={null}
{
  "id": "resp_051e00420efb9e150069aff6a18418819591abb7ce5f8487ed",
  "object": "response",
  "created_at": 1773139617,
  "status": "completed",
  "background": false,
  "completed_at": 1773139621,
  "content_filters": [
    {
      "blocked": false,
      "source_type": "completion",
      "content_filter_raw": [],
      "content_filter_results": {},
      "content_filter_offsets": {
        "start_offset": 0,
        "end_offset": 1147,
        "check_offset": 0
      }
    }
  ],
  "error": null,
  "frequency_penalty": 0.0,
  "incomplete_details": null,
  "instructions": null,
  "max_output_tokens": 5000,
  "max_tool_calls": null,
  "model": "gpt-54",
  "output": [
    {
      "id": "rs_051e00420efb9e150069aff6a32f948195996db3ff98314ef2",
      "type": "reasoning",
      "summary": []
    },
    {
      "id": "msg_051e00420efb9e150069aff6a33d808195825716a666d8ba8b",
      "type": "message",
      "status": "completed",
      "role": "assistant",
      "content": [
        {
          "type": "output_text",
          "annotations": [],
          "logprobs": [],
          "text": "The sky looks blue because of how sunlight interacts with Earth’s atmosphere.\n\n1. **Sunlight isn’t just “white”**\nSunlight is made of many colors (red, orange, yellow, green, blue, violet), each with different wavelengths.\n\n2. **Air scatters short wavelengths more**\nAs sunlight passes through the atmosphere, it hits gas molecules and tiny particles.\n- Shorter wavelengths (blue, violet) are scattered in all directions much more than longer wavelengths (red, orange).\n- This effect is called **Rayleigh scattering**.\n\n3. **We see more blue than violet**\n- Our eyes are more sensitive to blue than to violet.\n- Some violet light is also absorbed higher in the atmosphere.\nSo the scattered light we perceive is mostly blue.\n\n4. **Why sunsets are red/orange**\nAt sunrise and sunset, sunlight passes through much more atmosphere.\n- Most of the blue light gets scattered out of the direct path.\n- The remaining light reaching your eyes from the Sun is richer in reds and oranges."
        }
      ]
    }
  ],
  "parallel_tool_calls": true,
  "presence_penalty": 0.0,
  "previous_response_id": null,
  "prompt_cache_key": null,
  "prompt_cache_retention": null,
  "reasoning": {
    "effort": "high",
    "summary": null
  },
  "safety_identifier": null,
  "service_tier": "default",
  "store": true,
  "temperature": 1.0,
  "text": {
    "format": {
      "type": "text"
    },
    "verbosity": "medium"
  },
  "tool_choice": "auto",
  "tools": [],
  "top_logprobs": 0,
  "top_p": 1.0,
  "truncation": "disabled",
  "usage": {
    "input_tokens": 35,
    "input_tokens_details": {
      "cached_tokens": 0
    },
    "output_tokens": 267,
    "output_tokens_details": {
      "reasoning_tokens": 29
    },
    "total_tokens": 302
  },
  "user": null,
  "metadata": {}
}
```

## Usage Recommendations

1. **Escolha the appropriate reasoning effort level**: Use `high` for complex issues and `low` for simple tasks.
2. **Consider token usage**: Reasoning will increase token consumption.
3. **Utilize streaming**: For longer reasoning chains, streaming can enhance user experience.
4. **Provide context**: Give the model sufficient context to enable effective reasoning.

***

Última atualização: 2026-06-01