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GPT-5 Thinking Mode

GPT-5 supports enabling thinking mode through the Responses API’s reasoning parameter, allowing the model to perform deep reasoning before answering. 
POST /v1/responses

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Basic Usage

curl https://crazyrouter.com/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "gpt-5",
    "input": "Analyze the time complexity of the following code and propose an optimization:\ndef two_sum(nums, target):\n    for i in range(len(nums)):\n        for j in range(i+1, len(nums)):\n            if nums[i] + nums[j] == target:\n                return [i, j]",
    "reasoning": {
      "effort": "high"
    }
  }'

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reasoning Parameter

FieldTypeDescription
effortstringReasoning depth: low (quick), medium (balanced), high (deep)
summarystringThinking summary: auto, concise, detailed

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effort Level Comparison

LevelUse CasesToken Consumption
lowSimple questions, factual queriesLow
mediumGeneral reasoning, code analysisMedium
highComplex math, deep analysisHigh

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Get Thinking Summary

Set the summary parameter to get a summary of the model’s thinking process:
Python
response = client.responses.create(
    model="gpt-5",
    input="Design a high-concurrency message queue system architecture",
    reasoning={
        "effort": "high",
        "summary": "detailed"
    }
)

# Output may contain thinking summary
for item in response.output:
    if item.type == "reasoning":
        print("Thinking process:", item.summary)
    elif item.type == "message":
        for content in item.content:
            if content.type == "output_text":
                print("Answer:", content.text)

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Streaming Thinking

Python
stream = client.responses.create(
    model="gpt-5",
    input="Explain why the P=NP problem is important",
    reasoning={"effort": "high", "summary": "concise"},
    stream=True
)

for event in stream:
    if event.type == "response.reasoning_summary_text.delta":
        print(f"[Thinking] {event.delta}", end="")
    elif event.type == "response.output_text.delta":
        print(event.delta, end="")

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Combined with Tools

Thinking mode can be used simultaneously with Function Calling and Web Search:
Python
response = client.responses.create(
    model="gpt-5",
    input="Analyze the current global AI chip market landscape and provide investment recommendations",
    reasoning={"effort": "high"},
    tools=[
        {"type": "web_search_preview"}
    ]
)

print(response.output_text)

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Combined with System Instructions

Python
response = client.responses.create(
    model="gpt-5",
    instructions="You are a senior software architect who specializes in analyzing system design problems.",
    input="Design a real-time chat system that supports millions of users",
    reasoning={"effort": "high"}
)
In thinking mode, the model consumes additional tokens for internal reasoning. The higher the effort, the more tokens consumed, but the answer quality is typically better.
Not all models support the reasoning parameter. Currently it is mainly supported by GPT-5 and o-series models. For unsupported models, this parameter will be ignored.