Building with extended thinking

Extended thinking gives Claude enhanced reasoning capabilities for complex tasks, while providing varying levels of transparency into its step-by-step thought process before it delivers its final answer.

Supported models

Extended thinking is supported in the following models:

• Claude Opus 4.6 (claude-opus-4-6) — adaptive thinking recommended; manual mode (type: "enabled") is deprecated
• Claude Opus 4.5 (claude-opus-4-5-20251101)
• Claude Opus 4.1 (claude-opus-4-1-20250805)
• Claude Opus 4 (claude-opus-4-20250514)
• Claude Sonnet 4.5 (claude-sonnet-4-5-20250929)
• Claude Sonnet 4 (claude-sonnet-4-20250514)
• Claude Sonnet 3.7 (claude-3-7-sonnet-20250219) (deprecated)
• Claude Haiku 4.5 (claude-haiku-4-5-20251001)

How extended thinking works

When extended thinking is turned on, Claude creates thinking content blocks where it outputs its internal reasoning. Claude incorporates insights from this reasoning before crafting a final response.

The API response will include thinking content blocks, followed by text content blocks.

Here's an example of the default response format:

{
  "content": [
    {
      "type": "thinking",
      "thinking": "Let me analyze this step by step...",
      "signature": "WaUjzkypQ2mUEVM36O2TxuC06KN8xyfbJwyem2dw3URve/op91XWHOEBLLqIOMfFG/UvLEczmEsUjavL...."
    },
    {
      "type": "text",
      "text": "Based on my analysis..."
    }
  ]
}

For more information about the response format of extended thinking, see the Messages API Reference.

How to use extended thinking

Here is an example of using extended thinking in the Messages API:

curl https://api.anthropic.com/v1/messages \
     --header "x-api-key: $ANTHROPIC_API_KEY" \
     --header "anthropic-version: 2023-06-01" \
     --header "content-type: application/json" \
     --data \
'{
    "model": "claude-sonnet-4-5",
    "max_tokens": 16000,
    "thinking": {
        "type": "enabled",
        "budget_tokens": 10000
    },
    "messages": [
        {
            "role": "user",
            "content": "Are there an infinite number of prime numbers such that n mod 4 == 3?"
        }
    ]
}'

To turn on extended thinking, add a thinking object, with the type parameter set to enabled and the budget_tokens to a specified token budget for extended thinking. For Claude Opus 4.6, we recommend using type: "adaptive" instead — see Adaptive thinking for details. While type: "enabled" with budget_tokens is still supported on Opus 4.6, it is deprecated and will be removed in a future release.

The budget_tokens parameter determines the maximum number of tokens Claude is allowed to use for its internal reasoning process. In Claude 4 and later models, this limit applies to full thinking tokens, and not to the summarized output. Larger budgets can improve response quality by enabling more thorough analysis for complex problems, although Claude may not use the entire budget allocated, especially at ranges above 32k.

budget_tokens must be set to a value less than max_tokens. However, when using interleaved thinking with tools, you can exceed this limit as the token limit becomes your entire context window (200k tokens).

Summarized thinking

With extended thinking enabled, the Messages API for Claude 4 models returns a summary of Claude's full thinking process. Summarized thinking provides the full intelligence benefits of extended thinking, while preventing misuse.

Here are some important considerations for summarized thinking:

• You're charged for the full thinking tokens generated by the original request, not the summary tokens.
• The billed output token count will not match the count of tokens you see in the response.
• The first few lines of thinking output are more verbose, providing detailed reasoning that's particularly helpful for prompt engineering purposes.
• As Anthropic seeks to improve the extended thinking feature, summarization behavior is subject to change.
• Summarization preserves the key ideas of Claude's thinking process with minimal added latency, enabling a streamable user experience and easy migration from Claude Sonnet 3.7 to Claude 4 and later models.
• Summarization is processed by a different model than the one you target in your requests. The thinking model does not see the summarized output.

Streaming thinking

You can stream extended thinking responses using server-sent events (SSE).

When streaming is enabled for extended thinking, you receive thinking content via thinking_delta events.

For more documention on streaming via the Messages API, see Streaming Messages.

Here's how to handle streaming with thinking:

curl https://api.anthropic.com/v1/messages \
     --header "x-api-key: $ANTHROPIC_API_KEY" \
     --header "anthropic-version: 2023-06-01" \
     --header "content-type: application/json" \
     --data \
'{
    "model": "claude-sonnet-4-5",
    "max_tokens": 16000,
    "stream": true,
    "thinking": {
        "type": "enabled",
        "budget_tokens": 10000
    },
    "messages": [
        {
            "role": "user",
            "content": "What is the greatest common divisor of 1071 and 462?"
        }
    ]
}'

Example streaming output:

event: message_start
data: {"type": "message_start", "message": {"id": "msg_01...", "type": "message", "role": "assistant", "content": [], "model": "claude-sonnet-4-5", "stop_reason": null, "stop_sequence": null}}

event: content_block_start
data: {"type": "content_block_start", "index": 0, "content_block": {"type": "thinking", "thinking": ""}}

event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": "I need to find the GCD of 1071 and 462 using the Euclidean algorithm.\n\n1071 = 2 × 462 + 147"}}

event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": "\n462 = 3 × 147 + 21\n147 = 7 × 21 + 0\n\nSo GCD(1071, 462) = 21"}}

// Additional thinking deltas...

event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "signature_delta", "signature": "EqQBCgIYAhIM1gbcDa9GJwZA2b3hGgxBdjrkzLoky3dl1pkiMOYds..."}}

event: content_block_stop
data: {"type": "content_block_stop", "index": 0}

event: content_block_start
data: {"type": "content_block_start", "index": 1, "content_block": {"type": "text", "text": ""}}

event: content_block_delta
data: {"type": "content_block_delta", "index": 1, "delta": {"type": "text_delta", "text": "The greatest common divisor of 1071 and 462 is **21**."}}

// Additional text deltas...

event: content_block_stop
data: {"type": "content_block_stop", "index": 1}

event: message_delta
data: {"type": "message_delta", "delta": {"stop_reason": "end_turn", "stop_sequence": null}}

event: message_stop
data: {"type": "message_stop"}

Extended thinking with tool use

Extended thinking can be used alongside tool use, allowing Claude to reason through tool selection and results processing.

When using extended thinking with tool use, be aware of the following limitations:

1. Tool choice limitation: Tool use with thinking only supports tool_choice: {"type": "auto"} (the default) or tool_choice: {"type": "none"}. Using tool_choice: {"type": "any"} or tool_choice: {"type": "tool", "name": "..."} will result in an error because these options force tool use, which is incompatible with extended thinking.

2. Preserving thinking blocks: During tool use, you must pass thinking blocks back to the API for the last assistant message. Include the complete unmodified block back to the API to maintain reasoning continuity.

Toggling thinking modes in conversations

You cannot toggle thinking in the middle of an assistant turn, including during tool use loops. The entire assistant turn should operate in a single thinking mode:

• If thinking is enabled, the final assistant turn should start with a thinking block.
• If thinking is disabled, the final assistant turn should not contain any thinking blocks

From the model's perspective, tool use loops are part of the assistant turn. An assistant turn doesn't complete until Claude finishes its full response, which may include multiple tool calls and results.

For example, this sequence is all part of a single assistant turn:

User: "What's the weather in Paris?"
Assistant: [thinking] + [tool_use: get_weather]
User: [tool_result: "20°C, sunny"]
Assistant: [text: "The weather in Paris is 20°C and sunny"]

Even though there are multiple API messages, the tool use loop is conceptually part of one continuous assistant response.

Graceful thinking degradation

When a mid-turn thinking conflict occurs (such as toggling thinking on or off during a tool use loop), the API automatically disables thinking for that request. To preserve model quality and remain on-distribution, the API may:

• Strip thinking blocks from the conversation when they would create an invalid turn structure
• Disable thinking for the current request when the conversation history is incompatible with thinking being enabled

This means that attempting to toggle thinking mid-turn won't cause an error, but thinking will be silently disabled for that request. To confirm whether thinking was active, check for the presence of thinking blocks in the response.

Practical guidance

Best practice: Plan your thinking strategy at the start of each turn rather than trying to toggle mid-turn.

Example: Toggling thinking after completing a turn

User: "What's the weather?"
Assistant: [tool_use] (thinking disabled)
User: [tool_result]
Assistant: [text: "It's sunny"]
User: "What about tomorrow?"
Assistant: [thinking] + [text: "..."] (thinking enabled - new turn)

By completing the assistant turn before toggling thinking, you ensure that thinking is actually enabled for the new request.

Preserving thinking blocks

During tool use, you must pass thinking blocks back to the API, and you must include the complete unmodified block back to the API. This is critical for maintaining the model's reasoning flow and conversation integrity.

When Claude invokes tools, it is pausing its construction of a response to await external information. When tool results are returned, Claude will continue building that existing response. This necessitates preserving thinking blocks during tool use, for a couple of reasons:

1. Reasoning continuity: The thinking blocks capture Claude's step-by-step reasoning that led to tool requests. When you post tool results, including the original thinking ensures Claude can continue its reasoning from where it left off.

2. Context maintenance: While tool results appear as user messages in the API structure, they're part of a continuous reasoning flow. Preserving thinking blocks maintains this conceptual flow across multiple API calls. For more information on context management, see our guide on context windows.

Important: When providing thinking blocks, the entire sequence of consecutive thinking blocks must match the outputs generated by the model during the original request; you cannot rearrange or modify the sequence of these blocks.

Interleaved thinking

Extended thinking with tool use in Claude 4 models supports interleaved thinking, which enables Claude to think between tool calls and make more sophisticated reasoning after receiving tool results.

With interleaved thinking, Claude can:

• Reason about the results of a tool call before deciding what to do next
• Chain multiple tool calls with reasoning steps in between
• Make more nuanced decisions based on intermediate results

For Claude Opus 4.6, interleaved thinking is automatically enabled when using adaptive thinking — no beta header is needed.

For Claude 4 models, add the beta header interleaved-thinking-2025-05-14 to your API request to enable interleaved thinking.

Here are some important considerations for interleaved thinking:

• With interleaved thinking, the budget_tokens can exceed the max_tokens parameter, as it represents the total budget across all thinking blocks within one assistant turn.
• Interleaved thinking is only supported for tools used via the Messages API.
• For Claude 4 models, interleaved thinking requires the beta header interleaved-thinking-2025-05-14.
• Direct calls to the Claude API allow you to pass interleaved-thinking-2025-05-14 in requests to any model, with no effect.
• On 3rd-party platforms (e.g., Amazon Bedrock and Vertex AI), if you pass interleaved-thinking-2025-05-14 to any model aside from Claude Opus 4.6, Claude Opus 4.5, Claude Opus 4.1, Opus 4, or Sonnet 4, your request will fail.

Extended thinking with prompt caching

Prompt caching with thinking has several important considerations:

Thinking block context removal

• Thinking blocks from previous turns are removed from context, which can affect cache breakpoints
• When continuing conversations with tool use, thinking blocks are cached and count as input tokens when read from cache
• This creates a tradeoff: while thinking blocks don't consume context window space visually, they still count toward your input token usage when cached
• If thinking becomes disabled and you pass thinking content in the current tool use turn, the thinking content will be stripped and thinking will remain disabled for that request

Cache invalidation patterns

• Changes to thinking parameters (enabled/disabled or budget allocation) invalidate message cache breakpoints
• Interleaved thinking amplifies cache invalidation, as thinking blocks can occur between multiple tool calls
• System prompts and tools remain cached despite thinking parameter changes or block removal

Understanding thinking block caching behavior

When using extended thinking with tool use, thinking blocks exhibit specific caching behavior that affects token counting:

How it works:

1. Caching only occurs when you make a subsequent request that includes tool results
2. When the subsequent request is made, the previous conversation history (including thinking blocks) can be cached
3. These cached thinking blocks count as input tokens in your usage metrics when read from the cache
4. When a non-tool-result user block is included, all previous thinking blocks are ignored and stripped from context

Detailed example flow:

Request 1:

User: "What's the weather in Paris?"

Response 1:

[thinking_block_1] + [tool_use block 1]

Request 2:

User: ["What's the weather in Paris?"], 
Assistant: [thinking_block_1] + [tool_use block 1], 
User: [tool_result_1, cache=True]

Response 2:

[thinking_block_2] + [text block 2]

Request 2 writes a cache of the request content (not the response). The cache includes the original user message, the first thinking block, tool use block, and the tool result.

Request 3:

User: ["What's the weather in Paris?"],
Assistant: [thinking_block_1] + [tool_use block 1],
User: [tool_result_1, cache=True],
Assistant: [thinking_block_2] + [text block 2],
User: [Text response, cache=True]

For Claude Opus 4.5 and later (including Claude Opus 4.6), all previous thinking blocks are kept by default. For older models, because a non-tool-result user block was included, all previous thinking blocks are ignored. This request will be processed the same as:

User: ["What's the weather in Paris?"],
Assistant: [tool_use block 1],
User: [tool_result_1, cache=True],
Assistant: [text block 2],
User: [Text response, cache=True]

Key points:

• This caching behavior happens automatically, even without explicit cache_control markers
• This behavior is consistent whether using regular thinking or interleaved thinking

Max tokens and context window size with extended thinking

In older Claude models (prior to Claude Sonnet 3.7), if the sum of prompt tokens and max_tokens exceeded the model's context window, the system would automatically adjust max_tokens to fit within the context limit. This meant you could set a large max_tokens value and the system would silently reduce it as needed.

With Claude 3.7 and 4 models, max_tokens (which includes your thinking budget when thinking is enabled) is enforced as a strict limit. The system will now return a validation error if prompt tokens + max_tokens exceeds the context window size.

The context window with extended thinking

When calculating context window usage with thinking enabled, there are some considerations to be aware of:

• Thinking blocks from previous turns are stripped and not counted towards your context window
• Current turn thinking counts towards your max_tokens limit for that turn

The diagram below demonstrates the specialized token management when extended thinking is enabled:

The effective context window is calculated as:

context window =
  (current input tokens - previous thinking tokens) +
  (thinking tokens + encrypted thinking tokens + text output tokens)

We recommend using the token counting API to get accurate token counts for your specific use case, especially when working with multi-turn conversations that include thinking.

The context window with extended thinking and tool use

When using extended thinking with tool use, thinking blocks must be explicitly preserved and returned with the tool results.

The effective context window calculation for extended thinking with tool use becomes:

context window =
  (current input tokens + previous thinking tokens + tool use tokens) +
  (thinking tokens + encrypted thinking tokens + text output tokens)

The diagram below illustrates token management for extended thinking with tool use:

Managing tokens with extended thinking

Given the context window and max_tokens behavior with extended thinking Claude 3.7 and 4 models, you may need to:

• More actively monitor and manage your token usage
• Adjust max_tokens values as your prompt length changes
• Potentially use the token counting endpoints more frequently
• Be aware that previous thinking blocks don't accumulate in your context window

This change has been made to provide more predictable and transparent behavior, especially as maximum token limits have increased significantly.

Thinking encryption

Full thinking content is encrypted and returned in the signature field. This field is used to verify that thinking blocks were generated by Claude when passed back to the API.

Here are some important considerations on thinking encryption:

• When streaming responses, the signature is added via a signature_delta inside a content_block_delta event just before the content_block_stop event.
• signature values are significantly longer in Claude 4 models than in previous models.
• The signature field is an opaque field and should not be interpreted or parsed - it exists solely for verification purposes.
• signature values are compatible across platforms (Claude APIs, Amazon Bedrock, and Vertex AI). Values generated on one platform will be compatible with another.

Thinking redaction

Occasionally Claude's internal reasoning will be flagged by our safety systems. When this occurs, we encrypt some or all of the thinking block and return it to you as a redacted_thinking block. redacted_thinking blocks are decrypted when passed back to the API, allowing Claude to continue its response without losing context.

When building customer-facing applications that use extended thinking:

• Be aware that redacted thinking blocks contain encrypted content that isn't human-readable
• Consider providing a simple explanation like: "Some of Claude's internal reasoning has been automatically encrypted for safety reasons. This doesn't affect the quality of responses."
• If showing thinking blocks to users, you can filter out redacted blocks while preserving normal thinking blocks
• Be transparent that using extended thinking features may occasionally result in some reasoning being encrypted
• Implement appropriate error handling to gracefully manage redacted thinking without breaking your UI

Here's an example showing both normal and redacted thinking blocks:

{
  "content": [
    {
      "type": "thinking",
      "thinking": "Let me analyze this step by step...",
      "signature": "WaUjzkypQ2mUEVM36O2TxuC06KN8xyfbJwyem2dw3URve/op91XWHOEBLLqIOMfFG/UvLEczmEsUjavL...."
    },
    {
      "type": "redacted_thinking",
      "data": "EmwKAhgBEgy3va3pzix/LafPsn4aDFIT2Xlxh0L5L8rLVyIwxtE3rAFBa8cr3qpPkNRj2YfWXGmKDxH4mPnZ5sQ7vB9URj2pLmN3kF8/dW5hR7xJ0aP1oLs9yTcMnKVf2wRpEGjH9XZaBt4UvDcPrQ..."
    },
    {
      "type": "text",
      "text": "Based on my analysis..."
    }
  ]
}

When passing thinking and redacted_thinking blocks back to the API in a multi-turn conversation, you must include the complete unmodified block back to the API for the last assistant turn. This is critical for maintaining the model's reasoning flow. We suggest always passing back all thinking blocks to the API. For more details, see the Preserving thinking blocks section.

Differences in thinking across model versions

The Messages API handles thinking differently across Claude Sonnet 3.7 and Claude 4 models, primarily in redaction and summarization behavior.

See the table below for a condensed comparison:

Thinking block preservation in Claude Opus 4.5 and later

Starting with Claude Opus 4.5 (and continuing in Claude Opus 4.6), thinking blocks from previous assistant turns are preserved in model context by default. This differs from earlier models, which remove thinking blocks from prior turns.

Benefits of thinking block preservation:

• Cache optimization: When using tool use, preserved thinking blocks enable cache hits as they are passed back with tool results and cached incrementally across the assistant turn, resulting in token savings in multi-step workflows
• No intelligence impact: Preserving thinking blocks has no negative effect on model performance

Important considerations:

• Context usage: Long conversations will consume more context space since thinking blocks are retained in context
• Automatic behavior: This is the default behavior for Claude Opus 4.5 and later models (including Opus 4.6)—no code changes or beta headers required
• Backward compatibility: To leverage this feature, continue passing complete, unmodified thinking blocks back to the API as you would for tool use

Pricing

For complete pricing information including base rates, cache writes, cache hits, and output tokens, see the pricing page.

The thinking process incurs charges for:

• Tokens used during thinking (output tokens)
• Thinking blocks from the last assistant turn included in subsequent requests (input tokens)
• Standard text output tokens

When using summarized thinking:

• Input tokens: Tokens in your original request (excludes thinking tokens from previous turns)
• Output tokens (billed): The original thinking tokens that Claude generated internally
• Output tokens (visible): The summarized thinking tokens you see in the response
• No charge: Tokens used to generate the summary

Best practices and considerations for extended thinking

Working with thinking budgets

• Budget optimization: The minimum budget is 1,024 tokens. We suggest starting at the minimum and increasing the thinking budget incrementally to find the optimal range for your use case. Higher token counts enable more comprehensive reasoning but with diminishing returns depending on the task. Increasing the budget can improve response quality at the tradeoff of increased latency. For critical tasks, test different settings to find the optimal balance. Note that the thinking budget is a target rather than a strict limit—actual token usage may vary based on the task.
• Starting points: Start with larger thinking budgets (16k+ tokens) for complex tasks and adjust based on your needs.
• Large budgets: For thinking budgets above 32k, we recommend using batch processing to avoid networking issues. Requests pushing the model to think above 32k tokens causes long running requests that might run up against system timeouts and open connection limits.
• Token usage tracking: Monitor thinking token usage to optimize costs and performance.

Performance considerations

• Response times: Be prepared for potentially longer response times due to the additional processing required for the reasoning process. Factor in that generating thinking blocks may increase overall response time.
• Streaming requirements: The SDKs require streaming when max_tokens is greater than 21,333 to avoid HTTP timeouts on long-running requests. This is a client-side validation, not an API restriction. If you don't need to process events incrementally, use .stream() with .get_final_message() (Python) or .finalMessage() (TypeScript) to get the complete Message object without handling individual events — see Streaming Messages for details. When streaming, be prepared to handle both thinking and text content blocks as they arrive.

Feature compatibility

• Thinking isn't compatible with temperature or top_k modifications as well as forced tool use.
• When thinking is enabled, you can set top_p to values between 1 and 0.95.
• You cannot pre-fill responses when thinking is enabled.
• Changes to the thinking budget invalidate cached prompt prefixes that include messages. However, cached system prompts and tool definitions will continue to work when thinking parameters change.

Usage guidelines

• Task selection: Use extended thinking for particularly complex tasks that benefit from step-by-step reasoning like math, coding, and analysis.
• Context handling: You do not need to remove previous thinking blocks yourself. The Claude API automatically ignores thinking blocks from previous turns and they are not included when calculating context usage.
• Prompt engineering: Review our extended thinking prompting tips if you want to maximize Claude's thinking capabilities.

Next steps