Reasoning Models

For models with chain-of-thought (DeepSeek-R1, Qwen3), control reasoning depth with the reasoning_effort parameter.

Reasoning effort levels

response = client.chat.completions.create(
    model="forii/deepseek-r1",
    messages=[{"role": "user", "content": "Solve: x² - 5x + 6 = 0"}],
    reasoning_effort="high",  # none | low | medium | high
)

Level	Behavior	Use case
`none`	No chain-of-thought	Simple lookups, classification
`low`	Brief reasoning	Straightforward tasks
`medium`	Moderate reasoning	Standard problems
`high`	Deep reasoning	Math, logic, complex analysis

The reasoning_effort parameter is a Forii extension. It maps to the model’s internal chain-of-thought configuration. Higher effort = more tokens = higher cost. Use none or low for simple tasks to save credits.

Reading reasoning output

The model’s chain-of-thought is returned in the reasoning_content field:

response = client.chat.completions.create(
    model="forii/deepseek-r1",
    messages=[{"role": "user", "content": "Solve: x² - 5x + 6 = 0"}],
    reasoning_effort="high",
)

# The final answer
print(response.choices[0].message.content)
# "The solutions are x = 2 and x = 3."

# The chain-of-thought (reasoning process)
print(response.choices[0].message.reasoning_content)
# "Let me factor the quadratic: x² - 5x + 6 = (x-2)(x-3) = 0..."

Available reasoning models

Model	Reasoning support
`forii/deepseek-r1`	Full chain-of-thought with `reasoning_effort`
`forii/qwen3`	Full chain-of-thought with `reasoning_effort`
`forii/deepseek-v3`	Not a reasoning model — `reasoning_effort` has no effect

reasoning_effort only affects reasoning models. On standard models like DeepSeek-V3, it is silently ignored.

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Reasoning effort levels

Reading reasoning output

Available reasoning models

​Reasoning effort levels

​Reading reasoning output

​Available reasoning models

Reasoning effort levels

Reading reasoning output

Available reasoning models