Interleaved Thinking Supporty

Last Updated: 2025-12-03
Status: Supported (OpenAI-Compatible)

1. Overview

Interleaved Thinking is an advanced reasoning framework that enables models to perform explicit reasoning steps between tool calls. Models with Interleaved Thinking can:

Reflect on the current environment and tool outputs
Decide the next action based on updated reasoning
Maintain a continuous reasoning chain across multiple tool invocations
Provide transparent, inspectable multi-step thinking through reasoning_details or reasoning_content

This capability transforms traditional function-calling into agent-level tool use, making complex workflows more accurate, reliable, and context-aware. Novita fully supports Interleaved Thinking for all models that natively expose reasoning streams (e.g., MiniMax-M2 and other OpenAI-compatible reasoning models).

2. Key Concepts

2.1 Interleaving

Instead of executing a single reasoning phase followed by a tool call, the model performs:

Reason → Tool Call → Observe → Reason → Tool Call → ...

This allows the model to adjust its strategy dynamically based on previous tool outputs.

2.2 Reasoning Details (`reasoning_details`)

For some models, the content of the model’s thinking will be returned in the form of a separate structure:

"reasoning_details": [
  {
    "type": "reasoning.text",
    "format": "openai-responses-v1",
    "text": "Model’s step-by-step reasoning..."
  }
]

For such models, Novita supports returning this field in both streaming and non-streaming modes.

2.3 Conversation Memory Requirement

To maintain reasoning continuity: You must append the model’s full response including reasoning_details, tool_calls, and content to subsequent messages. Failing to preserve the chain may result in:

Incorrect tool use
Lost reasoning context
Repeated or circular tool calls
Reduced reliability

This requirement mirrors OpenAI’s reasoning APIs.

3. API Behavior

3.1 Request Format

No changes are required on the user side. Interleaved Thinking works with the standard OpenAI-compatible Chat Completions API.

3.2 Response Format

The model may return the following fields:

reasoning_content: original thinking content
reasoning_details: structured reasoning segments, this field is optioanl
tool_calls: tool invocation plan
content: natural language output

These extend the standard OpenAI format.

4. Example Request (MiniMax-M2)

{
  "model": "minimax/minimax-m2",
  "messages": [
    {
      "role": "user",
      "content": "How's the weather in San Francisco?"
    },
    {
      "role": "assistant",
      "name": "MiniMax AI",
      "content": "",
      "tool_calls": [
        {
          "id": "call_function_asqvfevfc8af_1",
          "type": "function",
          "function": {
            "name": "get_weather",
            "arguments": "{\"location\": \"San Francisco, US\"}"
          }
        }
      ],
      "reasoning_details": [
        {
          "type": "reasoning.text",
          "id": "reasoning-text-1",
          "format": "openai-responses-v1",
          "index": 0,
          "text": "The user is asking about the weather in San Francisco..."
        }
      ]
    },
    {
      "role": "tool",
      "tool_call_id": "call_function_asqvfevfc8af_1",
      "content": "24℃, sunny"
    }
  ],
  "stream": true,
  "reasoning_split": true,
  "max_tokens": 1024,
  "temperature": 0.7,
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "get_weather",
        "description": "Get weather for a specific location",
        "parameters": {
          "type": "object",
          "properties": {
            "location": { "type": "string" }
          },
          "required": ["location"]
        }
      }
    }
  ]
}

5. Example Response (Non-Streaming)

{
  "id": "07a4dedfdb1498b045498dfd42497639",
  "object": "chat.completion",
  "created": 1764303147,
  "model": "MiniMax",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "",
        "name": "MiniMax",
        "tool_calls": [
          {
            "index": 0,
            "id": "call_function_9w7wq1j9zmpl_1",
            "type": "function",
            "function": {
              "name": "get_weather",
              "arguments": "{\"location\": \"ShangHai\"}"
            }
          }
        ],
        "reasoning_content": "The user asked for Shanghai weather...",
        "reasoning_details": [
          {
            "type": "reasoning.text",
            "text": "The user is asking about the weather in Shanghai...",
            "id": "reasoning-text-1",
            "format": "openai-responses-v1",
            "index": 0
          }
        ]
      },
      "finish_reason": "tool_calls"
    }
  ]
}

6. Streaming Response Example

{
  "id": "664c5ad870c1888fbcbd267d9829e354",
  "object": "chat.completion.chunk",
  "created": 1764303504,
  "model": "minimax-m2",
  "choices": [
    {
      "index": 0,
      "delta": {
        "role": "assistant",
        "reasoning_content": "...\n\nThe user has specifically asked...",
        "reasoning_details": [
          {
            "type": "reasoning.text",
            "text": ".\n\nThe user has specifically asked...",
            "id": "reasoning-text-1",
            "format": "openai-responses-v1",
            "index": 0
          }
        ]
      },
      "finish_reason": null
    }
  ]
}

7. Developer Notes

7.1 Models That Support Interleaved Thinking

All models exposing reasoning_details through OpenAI-compatible APIs, including:

MiniMax-M2
(Upcoming) Novita Reasoning Series
Other reasoning-enabled partner models

7.2 Pricing

Billing is based on reasoning tokens, following the model’s pricing rules. reasoning_details will increase token usage.

7.3 Error Handling

You may encounter:

Missing tool parameters
Recursive or repeated tool calls
Incorrect assumptions in the reasoning phase

Ensure your application validates tool arguments and handles model errors gracefully.

8. Best Practices

✓ Always include full model messages in the next request Include:

content
tool_calls
reasoning_details

✓ Enable streaming for long-chain reasoning Streaming allows the client to:

Monitor the reasoning process
Detect incorrect tool plans early
Provide faster user feedback

✓ Combine with server-side state machines for stability For production systems, we recommend pairing Interleaved Thinking with deterministic guardrails, e.g.:

Parameter validators
Execution sandbox
Maximum recursion safeguards

9. Summary

Interleaved Thinking significantly enhances multi-step reasoning and tool-use reliability:

Transparent and inspectable step-wise reasoning
Adaptive planning between tool invocations
Stronger context retention across long workflows
Fully compatible with OpenAI-style Chat Completions API

Novita will continue expanding support for advanced reasoning models, bringing agent-level intelligence to the API layer.

Get started

Model APIs

Agent Sandbox

GPUs

Observability

Resources

Interleaved Thinking Supporty

1. Overview

2. Key Concepts

2.1 Interleaving

2.2 Reasoning Details (`reasoning_details`)

2.3 Conversation Memory Requirement

3. API Behavior

3.1 Request Format

3.2 Response Format

4. Example Request (MiniMax-M2)

5. Example Response (Non-Streaming)

6. Streaming Response Example

7. Developer Notes

7.1 Models That Support Interleaved Thinking

7.2 Pricing

7.3 Error Handling

8. Best Practices

9. Summary

​1. Overview

​2. Key Concepts

​2.1 Interleaving

​2.2 Reasoning Details (reasoning_details)

​2.3 Conversation Memory Requirement

​3. API Behavior

​3.1 Request Format

​3.2 Response Format

​4. Example Request (MiniMax-M2)

​5. Example Response (Non-Streaming)

​6. Streaming Response Example

​7. Developer Notes

​7.1 Models That Support Interleaved Thinking

​7.2 Pricing

​7.3 Error Handling

​8. Best Practices

​9. Summary

1. Overview

2. Key Concepts

2.1 Interleaving

2.2 Reasoning Details (`reasoning_details`)

2.3 Conversation Memory Requirement

3. API Behavior

3.1 Request Format

3.2 Response Format

4. Example Request (MiniMax-M2)

5. Example Response (Non-Streaming)

6. Streaming Response Example

7. Developer Notes

7.1 Models That Support Interleaved Thinking

7.2 Pricing

7.3 Error Handling

8. Best Practices

9. Summary