Reasoning and Interleaved Thinking

For models that support it, the BLACKBOX AI API can return Reasoning Tokens, also known as thinking tokens. BLACKBOX AI 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 tokens. While most models and providers make reasoning tokens available in the response, some (like the OpenAI o-series) do not.

Important: Interleaved thinking increases token usage and response latency. Consider your budget and performance requirements when enabling this feature.

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 "xhigh", "high", "medium", "low", "minimal" or "none" (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.

Max Tokens for Reasoning

Supported models 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.

Reasoning Effort Level

Supported models Currently supported by OpenAI reasoning models (o1 series, o3 series, GPT-5 series) and Grok models

"effort": "xhigh" - Allocates the largest portion of tokens for reasoning (approximately 95% of max_tokens)
"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)
"effort": "minimal" - Allocates an even smaller portion of tokens (approximately 10% of max_tokens)
"effort": "none" - Disables reasoning entirely

For models that only support reasoning.max_tokens, the effort level will be set based on the percentages above.

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.

Enable Reasoning with Default Config

To enable reasoning with the default parameters:

"enabled": true - Enables reasoning at the “medium” effort level with no exclusions.

Examples

Basic Usage with Reasoning Tokens

from openai import OpenAI
client = OpenAI(
    base_url="https://api.blackbox.ai/chat/completions",
    api_key="<BLACKBOX_API_KEY>",
)
response = client.chat.completions.create(
    model="blackboxai/openai/o3-mini",
    messages=[
        {"role": "user", "content": "How would you build the world's tallest skyscraper?"}
    ],
    extra_body={
        "reasoning": {
            "effort": "high"
        }
    },
)
msg = response.choices[0].message
print(getattr(msg, "reasoning", None))

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:

from openai import OpenAI
client = OpenAI(
    base_url="https://api.blackbox.ai/chat/completions",
    api_key="<BLACKBOX_API_KEY>",
)
response = client.chat.completions.create(
    model="blackboxai/anthropic/claude-sonnet-4.5",
    messages=[
        {"role": "user", "content": "What's the most efficient algorithm for sorting a large dataset?"}
    ],
    extra_body={
        "reasoning": {
            "max_tokens": 2000
        }
    },
)
msg = response.choices[0].message
print(getattr(msg, "reasoning", None))
print(getattr(msg, "content", None))

Excluding Reasoning Tokens from Response

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

from openai import OpenAI
client = OpenAI(
    base_url="https://api.blackbox.ai/chat/completions",
    api_key="<BLACKBOX_API_KEY>",
)
response = client.chat.completions.create(
    model="blackboxai/deepseek/deepseek-r1",
    messages=[
        {"role": "user", "content": "Explain quantum computing in simple terms."}
    ],
    extra_body={
        "reasoning": {
            "effort": "high",
            "exclude": True
        }
    },
)
msg = response.choices[0].message
print(getattr(msg, "content", None))

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:

from openai import OpenAI
client = OpenAI(
    base_url="https://api.blackbox.ai/chat/completions",
    api_key="<BLACKBOX_API_KEY>",
)

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

def do_req(model: str, content: str, reasoning_config: dict | None = None):
    payload = {
        "model": model,
        "messages": [{"role": "user", "content": content}],
        "stop": "</think>",
    }
    if reasoning_config:
        payload.update(reasoning_config)
    return client.chat.completions.create(**payload)

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

# Let's test! Here's the naive response:
simple_response = do_req("blackboxai/openai/gpt-4o-mini", question)
print(getattr(simple_response.choices[0].message, "content", None))

# Here's the response with the reasoning token injected:
content = f"{question}. Here is some context to help you: {reasoning}"
smart_response = do_req("blackboxai/openai/gpt-4o-mini", content)
print(getattr(smart_response.choices[0].message, "content", None))

Preserving Reasoning

See API Best Practices for how to correctly pass reasoning blocks back across multi-turn tool calling conversations without signature errors.

To preserve reasoning context across multiple turns, you can pass it back to the API in one of two ways:

message.reasoning (string): Pass the plaintext reasoning as a string field on the assistant message
message.reasoning_details (array): Pass the full reasoning_details block

Use reasoning_details when working with models that return special reasoning types (such as encrypted or summarized) - this preserves the full structure needed for those models. For models that only return raw reasoning strings, you can use the simpler reasoning field. You can also use reasoning_content as an alias - it functions identically to reasoning.

Model Support

Preserving reasoning is currently supported by these proprietary models:

All OpenAI reasoning models (o1 series, o3 series, GPT-5 series and newer)
All Anthropic reasoning models (Claude 3.7 series and newer)
All Gemini Reasoning models
All xAI reasoning models

And these open source models:

MiniMax M2 / M2.1
Kimi K2 Thinking / K2.5
INTELLECT-3
Nemotron 3 Nano
MiMo-V2-Flash
All Z.ai reasoning models (GLM 4.5 series and newer)

Note: standard interleaved thinking only. The preserved thinking feature for Z.ai models is currently not supported.

The reasoning_details functionality works identically across all supported reasoning models. You can easily switch between OpenAI reasoning models (like blackboxai/openai/gpt-5.2) and Anthropic reasoning models (like blackboxai/anthropic/claude-sonnet-4.5) without changing your code structure. 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 Models: When 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.

Example: Preserving Reasoning Blocks with Tool Calls

from openai import OpenAI
client = OpenAI(
    base_url="https://api.blackbox.ai/chat/completions",
    api_key="<BLACKBOX_API_KEY>",
)

# Define tools once and reuse
tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get current weather",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {"type": "string"}
            },
            "required": ["location"]
        }
    }
}]

# First API call with tools
# Note: You can use 'blackboxai/openai/gpt-5.2' instead of 'blackboxai/anthropic/claude-sonnet-4.5' - they're completely interchangeable
response = client.chat.completions.create(
    model="blackboxai/anthropic/claude-sonnet-4.5",
    messages=[
        {"role": "user", "content": "What's the weather like in Boston? Then recommend what to wear."}
    ],
    tools=tools,
    extra_body={"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="blackboxai/anthropic/claude-sonnet-4.5",
    messages=messages,  # Includes preserved thinking blocks
    tools=tools
)

For more detailed information about thinking encryption, redacted blocks, and advanced use cases, see Anthropic’s documentation on extended thinking. For more information about OpenAI reasoning models, see OpenAI’s reasoning documentation.

Reasoning Details 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.

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

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
- "xai-responses-v1" - xAI responses format version 1
- "anthropic-claude-v1" - Anthropic Claude format version 1 (default)
index (number, optional): Sequential index of the reasoning detail

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
}

Response Examples

Non-Streaming Response

In non-streaming responses, reasoning_details appears in the message:

{
  "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
          }
        ]
      }
    }
  ]
}

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

Legacy Parameters

For backward compatibility, BLACKBOX AI 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.

Provider-Specific Reasoning Implementation

Anthropic Models with Reasoning Tokens

The latest Claude models, such as blackboxai/anthropic/claude-3.7-sonnet, support working with and returning reasoning tokens. You can enable reasoning on Anthropic models only using the unified reasoning parameter with either effort or max_tokens.

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 128,000 tokens maximum and 1024 tokens minimum. The formula for calculating the budget_tokens is: budget_tokens = max(min(max_tokens * {effort_ratio}, 128000), 1024) effort_ratio is 0.95 for xhigh effort, 0.8 for high effort, 0.5 for medium effort, 0.2 for low effort, and 0.1 for minimal effort.

Important: max_tokens must be strictly higher than the reasoning budget to ensure there are tokens available for the final response after thinking.

Example: Streaming with Anthropic Reasoning Tokens

from openai import OpenAI
client = OpenAI(
    base_url="https://api.blackbox.ai/chat/completions",
    api_key="<BLACKBOX_API_KEY>",
)

def chat_completion_with_reasoning(messages):
    response = client.chat.completions.create(
        model="blackboxai/anthropic/claude-3.7-sonnet",
        messages=messages,
        max_tokens=10000,
        extra_body={
            "reasoning": {
                "max_tokens": 8000
            }
        },
        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_details') and chunk.choices[0].delta.reasoning_details:
        print(f"REASONING_DETAILS: {chunk.choices[0].delta.reasoning_details}")
    elif getattr(chunk.choices[0].delta, 'content', None):
        print(f"CONTENT: {chunk.choices[0].delta.content}")

Google Gemini 3 Models with Thinking Levels

Gemini 3 models (such as blackboxai/google/gemini-3-pro-preview and blackboxai/google/gemini-3-flash-preview) use Google’s thinkingLevel API instead of the older thinkingBudget API used by Gemini 2.5 models. BLACKBOX AI maps the reasoning.effort parameter directly to Google’s thinkingLevel values:

BLACKBOX AI reasoning.effort	Google thinkingLevel
”minimal"	"minimal"
"low"	"low"
"medium"	"medium"
"high"	"high"
"xhigh"	"high” (mapped down)

Token Consumption is Determined by Google

When using thinkingLevel, the actual number of reasoning tokens consumed is determined internally by Google. There are no publicly documented token limit breakpoints for each level. For example, setting effort: "low" might result in several hundred reasoning tokens depending on the complexity of the task. This is expected behavior and reflects how Google implements thinking levels internally. If a model doesn’t support a specific effort level (for example, if a model only supports low and high), BLACKBOX AI will map your requested effort to the nearest supported level.

Using max_tokens with Gemini 3

If you specify reasoning.max_tokens explicitly, BLACKBOX AI will pass it through as thinkingBudget to Google’s API. However, for Gemini 3 models, Google internally maps this budget value to a thinkingLevel, so you will not get precise token control. The actual token consumption is still determined by Google’s thinkingLevel implementation, not by the specific budget value you provide.

Example: Using Thinking Levels with Gemini 3

from openai import OpenAI
client = OpenAI(
    base_url="https://api.blackbox.ai/chat/completions",
    api_key="<BLACKBOX_API_KEY>",
)

response = client.chat.completions.create(
    model="blackboxai/google/gemini-3-pro-preview",
    messages=[
        {"role": "user", "content": "Explain the implications of quantum entanglement."}
    ],
    extra_body={
        "reasoning": {
            "effort": "low"  # Maps to thinkingLevel: "low"
        }
    },
)

msg = response.choices[0].message
print(getattr(msg, "reasoning", None))

Reasoning with the Responses API

Reasoning models like blackboxai/openai/gpt-5.3-codex are LLMs trained with reinforcement learning to perform reasoning. They think before they answer, producing a long internal chain of thought before responding to the user. Reasoning models excel at complex problem solving, coding, scientific reasoning, and multi-step planning for agentic workflows.

Get Started with Reasoning

Call the Responses API and specify your reasoning model and reasoning effort:

# Enterprise users: use https://enterprise.blackbox.ai/v1/responses
curl --location 'https://api.blackbox.ai/v1/responses' \
  --header 'Authorization: Bearer YOUR_BLACKBOX_API_KEY' \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "blackboxai/openai/gpt-5.3-codex",
    "store": false,
    "stream": false,
    "reasoning": {
      "effort": "medium"
    },
    "input": [{
      "role": "user",
      "content": "Write a bash script that takes a matrix represented as a string with format [1,2],[3,4],[5,6] and prints the transpose in the same format."
    }]
  }'

The reasoning.effort parameter guides the model on how many reasoning tokens to generate before creating a response. The default value is medium.

Value	Description
`"none"`	Disables reasoning entirely — no reasoning tokens are generated
`"low"`	Favors speed and economical token usage
`"medium"`	Balanced between speed and reasoning accuracy (default)
`"high"`	Favors more complete reasoning for complex tasks
`"xhigh"`	Maximum reasoning depth — allocates the largest portion of tokens for thinking

How Reasoning Works

Reasoning models introduce reasoning tokens in addition to input and output tokens. The models use these reasoning tokens to “think,” breaking down the prompt and considering multiple approaches to generating a response. After generating reasoning tokens, the model produces an answer as visible completion tokens and discards the reasoning tokens from its context.

While reasoning tokens are not visible via the API, they still occupy space in the model’s context window and are billed as output tokens.

Managing the Context Window

It’s important to ensure there’s enough space in the context window for reasoning tokens when creating responses. Depending on the problem’s complexity, the models may generate anywhere from a few hundred to tens of thousands of reasoning tokens. The exact number of reasoning tokens used is visible in the usage object of the response, under output_tokens_details:

{
  "usage": {
    "input_tokens": 75,
    "input_tokens_details": {
      "cached_tokens": 0
    },
    "output_tokens": 1186,
    "output_tokens_details": {
      "reasoning_tokens": 1024
    },
    "total_tokens": 1261
  }
}

Allocating Space for Reasoning

If the generated tokens reach the context window limit or the max_output_tokens value you’ve set, you’ll receive a response with a status of incomplete and incomplete_details with reason set to max_output_tokens. This might occur before any visible output tokens are produced, meaning you could incur costs for input and reasoning tokens without receiving a visible response. To prevent this, ensure there’s sufficient space in the context window or adjust the max_output_tokens value to a higher number. We recommend reserving at least 25,000 tokens for reasoning and outputs when you start experimenting with these models.

# Enterprise users: use https://enterprise.blackbox.ai/v1/responses
curl --location 'https://api.blackbox.ai/v1/responses' \
  --header 'Authorization: Bearer YOUR_BLACKBOX_API_KEY' \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "blackboxai/openai/gpt-5.3-codex",
    "store": false,
    "stream": false,
    "reasoning": {
      "effort": "medium"
    },
    "max_output_tokens": 300,
    "input": [{
      "role": "user",
      "content": "Write a bash script that takes a matrix represented as a string with format [1,2],[3,4],[5,6] and prints the transpose in the same format."
    }]
  }'

Reasoning Summaries

You can view a summary of the model’s reasoning using the summary parameter inside the reasoning object. Different models support different reasoning summary settings. To access the most detailed summarizer available for a model, set the value of this parameter to auto. auto will be equivalent to detailed for most reasoning models today, but there may be more granular settings in the future.

Value	Description
`"auto"`	Uses the most detailed summarizer available for the model (recommended)
`"detailed"`	Full step-by-step reasoning summary
`"concise"`	A shorter, high-level summary of the reasoning process

Reasoning summary output is part of the summary array in the reasoning output item. This output will not be included unless you explicitly opt in by setting the summary field.

# Enterprise users: use https://enterprise.blackbox.ai/v1/responses
curl --location 'https://api.blackbox.ai/v1/responses' \
  --header 'Authorization: Bearer YOUR_BLACKBOX_API_KEY' \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "blackboxai/openai/gpt-5.3-codex",
    "store": false,
    "stream": false,
    "reasoning": {
      "effort": "low",
      "summary": "auto"
    },
    "input": [{
      "role": "user",
      "content": "What is the capital of France?"
    }]
  }'

This API request will return an output array with both a reasoning summary and the assistant message:

[
  {
    "id": "rs_abc123",
    "type": "reasoning",
    "summary": [
      {
        "type": "summary_text",
        "text": "**Answering a simple question**\n\nThe capital of France is Paris — a well-known fact. I'll keep the answer brief and direct."
      }
    ]
  },
  {
    "id": "msg_abc456",
    "type": "message",
    "status": "completed",
    "role": "assistant",
    "content": [
      {
        "type": "output_text",
        "text": "The capital of France is Paris.",
        "annotations": []
      }
    ]
  }
]

Tool Calling

Use tools with reasoning models

Best Practices

Preserve reasoning signatures across turns and avoid common errors

Interleaved Thinking

Thinking between tool calls on the Messages API

Endpoints

​Controlling Reasoning Tokens

​Max Tokens for Reasoning

​Reasoning Effort Level

​Excluding Reasoning Tokens

​Enable Reasoning with Default Config

​Examples

​Basic Usage with Reasoning Tokens

​Using Max Tokens for Reasoning

​Excluding Reasoning Tokens from Response

​Advanced Usage: Reasoning Chain-of-Thought

​Preserving Reasoning

​Model Support

​Example: Preserving Reasoning Blocks with Tool Calls

​Reasoning Details API Shape

​reasoning_details Array Structure

​Common Fields

​Reasoning Detail Types

​1. Summary Type (reasoning.summary)

​2. Encrypted Type (reasoning.encrypted)

​3. Text Type (reasoning.text)

​Response Examples

​Non-Streaming Response

​Streaming Response

​Streaming Behavior Notes:

​Legacy Parameters

​Provider-Specific Reasoning Implementation

​Anthropic Models with Reasoning Tokens

​Reasoning Max Tokens for Anthropic Models

​Example: Streaming with Anthropic Reasoning Tokens

​Google Gemini 3 Models with Thinking Levels

​Token Consumption is Determined by Google

​Using max_tokens with Gemini 3

​Example: Using Thinking Levels with Gemini 3

​Reasoning with the Responses API

​Get Started with Reasoning

​How Reasoning Works

​Managing the Context Window

​Allocating Space for Reasoning

​Reasoning Summaries

Tool Calling

Best Practices

Interleaved Thinking

Controlling Reasoning Tokens

Max Tokens for Reasoning

Reasoning Effort Level

Excluding Reasoning Tokens

Enable Reasoning with Default Config

Examples

Basic Usage with Reasoning Tokens

Using Max Tokens for Reasoning

Excluding Reasoning Tokens from Response

Advanced Usage: Reasoning Chain-of-Thought

Preserving Reasoning

Model Support

Example: Preserving Reasoning Blocks with Tool Calls

Reasoning Details API Shape

reasoning_details Array Structure

Common Fields

Reasoning Detail Types

1. Summary Type (reasoning.summary)

2. Encrypted Type (reasoning.encrypted)

3. Text Type (reasoning.text)

Response Examples

Non-Streaming Response

Streaming Response

Streaming Behavior Notes:

Legacy Parameters

Provider-Specific Reasoning Implementation

Anthropic Models with Reasoning Tokens

Reasoning Max Tokens for Anthropic Models

Example: Streaming with Anthropic Reasoning Tokens

Google Gemini 3 Models with Thinking Levels

Token Consumption is Determined by Google

Using max_tokens with Gemini 3

Example: Using Thinking Levels with Gemini 3

Reasoning with the Responses API

Get Started with Reasoning

How Reasoning Works

Managing the Context Window

Allocating Space for Reasoning

Reasoning Summaries