What are reasoning models in the OpenAI API and what is the 'effort' parameter?

Reasoning models (originating with the o1/o3 family and now integrated into the GPT-5.x line) spend additional compute "thinking" through a problem before producing a final answer. This hidden chain-of-thought reasoning dramatically improves performance on complex multi-step tasks like mathematics, coding, and planning.

from openai import OpenAI
client = OpenAI()

# Responses API with reasoning effort
result = client.responses.create(
    model="gpt-5.5",
    input="Design a thread-safe cache with LRU eviction in Python.",
    reasoning={"effort": "high"},   # "low" | "medium" | "high"
)
print(result.output_text)

# "low"  - faster, cheaper; suitable for simple code Q&A
# "medium" - balanced; good default for most coding tasks
# "high" - maximum reasoning; for complex architecture, hard bugs

# Encrypted reasoning (reasoning without exposing thinking tokens):
result = client.responses.create(
    model="gpt-5.5",
    input="Find all race conditions in this concurrent code: ...",
    reasoning={"effort": "high", "summary": "auto"},  # get reasoning summary
)

Adaptive reasoning in gpt-5.4: this model dynamically adjusts reasoning depth per task. On simple requests it responds quickly (using far fewer tokens); on complex tasks it automatically uses more compute. Testing showed gpt-5.4 uses 93.7% fewer tokens than gpt-5.5 for the bottom 10% of simple user turns, while maintaining full capability on hard tasks.

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