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Some models perform internal chain-of-thought reasoning before generating a final response. These reasoning steps consume tokens — called reasoning tokens — which affect cost and latency but are not visible to the user by default.

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Supported Models

ModelReasoning Support
openai/o4-miniAlways-on reasoning
openai/o3Always-on reasoning
anthropic/claude-sonnet-4-6Optional extended thinking
anthropic/claude-opus-4-6Optional extended thinking
deepseek/deepseek-r1Always-on reasoning
google/gemini-2.5-proOptional thinking mode
google/gemini-2.5-flashOptional thinking mode

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How Reasoning Tokens Appear in Usage

Reasoning tokens are reported in the usage object as part of completion_tokens_details: 
{
  "usage": {
    "prompt_tokens": 150,
    "completion_tokens": 520,
    "total_tokens": 670,
    "completion_tokens_details": {
      "reasoning_tokens": 400
    }
  }
}
In this example, 400 of the 520 completion tokens were used for internal reasoning. Only the remaining 120 tokens appear in the visible response.

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Billing for Reasoning Tokens

Reasoning tokens are billed at the completion token rate for that model. They are included in completion_tokens for billing purposes — the breakdown is informational. ARouter passes through the upstream provider’s reasoning token counts without modification.

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Controlling Reasoning Behavior

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OpenAI o-series (o4-mini, o3)

Reasoning is always on for o-series models. Use reasoning_effort to control how much reasoning the model does: 
{
  "model": "openai/o4-mini",
  "reasoning_effort": "high",
  "messages": [...]
}
Valid values: "low", "medium", "high". Higher effort = more reasoning tokens = higher quality and cost.

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Anthropic Extended Thinking

Enable extended thinking by passing thinking in your request: 
import anthropic

client = anthropic.Anthropic(
    api_key="your-arouter-key",
    base_url="https://api.arouter.ai/anthropic",
)

response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=16000,
    thinking={
        "type": "enabled",
        "budget_tokens": 10000,
    },
    messages=[{"role": "user", "content": "Solve this step by step: ..."}],
)
budget_tokens caps how many tokens can be used for thinking. The thinking content is returned as a separate block in the response.

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DeepSeek R1

Reasoning is always on for DeepSeek R1. The model returns a reasoning_content field alongside the regular content: 
from openai import OpenAI

client = OpenAI(
    api_key="your-arouter-key",
    base_url="https://api.arouter.ai/v1",
)

response = client.chat.completions.create(
    model="deepseek/deepseek-r1",
    messages=[{"role": "user", "content": "Prove that √2 is irrational."}],
)

# Reasoning content (if exposed by provider)
print(response.choices[0].message.reasoning_content)
# Final answer
print(response.choices[0].message.content)

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Google Gemini Thinking

Enable thinking for Gemini 2.5 models via the thinking parameter: 
response = client.chat.completions.create(
    model="google/gemini-2.5-flash",
    messages=[...],
    extra_body={
        "thinking": {
            "type": "enabled",
            "budget_tokens": 5000
        }
    }
)

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Activity Export and Reasoning Tokens

The Activity Export includes a breakdown of reasoning tokens, so you can accurately track their contribution to total costs. Reasoning tokens are included in completion tokens in the export summary.

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Best Practices

  • Start with "low" or "medium" effort for o-series models unless you need maximum reasoning quality. This reduces cost and latency significantly.
  • Set a budget_tokens cap for Anthropic and Gemini thinking models to avoid unexpectedly large bills on complex queries.
  • Monitor reasoning token ratios in your activity feed. A high ratio of reasoning to output tokens is normal for complex tasks but may indicate the model is overthinking simple queries.
  • Don’t disable reasoning to save costs on tasks that genuinely require multi-step reasoning — output quality degrades significantly.