What's happening?
According to https://openrouter.ai/docs/guides/best-practices/reasoning-tokens
OpenRouter normalizes the different ways of customizing the amount of reasoning tokens that the model will use, providing a unified interface across different providers.
"effort": "high", // Can be "xhigh", "high", "medium", "low", "minimal" or "none" (OpenAI-style)
So as long as the model support reasoning, presumably all xhigh,high,medium,low,minimal,none values can be used.
I tried xhigh,minimal,none with openai/o1 model on OpenRouter, and they worked, even though official OpenAI API should support only high,medium,low for o1 model, which is an earlier model than gpt-5 (when new values started to be introduced).
C:\test>curl https://openrouter.ai/api/v1/chat/completions -H "Content-Type: application/json" -H "Authorization: Bearer sk-<my_key>" -d "{"model": "openai/o1", "messages": [{"role": "user", "content": "Hi!"}], "reasoning_effort": "xhigh"}"
{"id":"gen-NNN","provider":"OpenAI","model":"openai/o1","object":"chat.completion","created":NNN,"choices":[{"logprobs":null,"finish_reason":"stop","native_finish_reason":"completed","index":0,"message":{"role":"assistant","content":"Hi there! How can I help you today?","refusal":null,"reasoning":null}}],"usage":{"prompt_tokens":8,"completion_tokens":11,"total_tokens":19,"cost":0.00078,"is_byok":false,"prompt_tokens_details":{"cached_tokens":0},"cost_details":{"upstream_inference_cost":0.00078,"upstream_inference_prompt_cost":0.00012,"upstream_inference_completions_cost":0.00066},"completion_tokens_details":{"reasoning_tokens":0,"image_tokens":0}}}
C:\test>curl https://openrouter.ai/api/v1/chat/completions -H "Content-Type: application/json" -H "Authorization: Bearer sk-<my_key>" -d "{"model": "openai/o1", "messages": [{"role": "user", "content": "Hi!"}], "reasoning_effort": "minimal"}"
{"id":"gen-NNN","provider":"OpenAI","model":"openai/o1","object":"chat.completion","created":NNN,"choices":[{"logprobs":null,"finish_reason":"stop","native_finish_reason":"completed","index":0,"message":{"role":"assistant","content":"Hello there! How can I help you today?","refusal":null,"reasoning":null}}],"usage":{"prompt_tokens":8,"completion_tokens":11,"total_tokens":19,"cost":0.00078,"is_byok":false,"prompt_tokens_details":{"cached_tokens":0},"cost_details":{"upstream_inference_cost":0.00078,"upstream_inference_prompt_cost":0.00012,"upstream_inference_completions_cost":0.00066},"completion_tokens_details":{"reasoning_tokens":0,"image_tokens":0}}}
C:\test>curl https://openrouter.ai/api/v1/chat/completions -H "Content-Type: application/json" -H "Authorization: Bearer sk-<my_key>" -d "{"model": "openai/o1", "messages": [{"role": "user", "content": "Hi!"}], "reasoning_effort": "none"}"
{"id":"gen-NNN","provider":"OpenAI","model":"openai/o1","object":"chat.completion","created":NNN,"choices":[{"logprobs":null,"finish_reason":"stop","native_finish_reason":"completed","index":0,"message":{"role":"assistant","content":"Hi there! How can I help you today?","refusal":null,"reasoning":null}}],"usage":{"prompt_tokens":8,"completion_tokens":11,"total_tokens":19,"cost":0.00078,"is_byok":false,"prompt_tokens_details":{"cached_tokens":0},"cost_details":{"upstream_inference_cost":0.00078,"upstream_inference_prompt_cost":0.00012,"upstream_inference_completions_cost":0.00066},"completion_tokens_details":{"reasoning_tokens":0,"image_tokens":0}}}
https://platform.openai.com/docs/api-reference/responses/create#responses_create-reasoning-effort says the following:
effort
string
Optional
Defaults to medium
Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, and xhigh. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response.
gpt-5.1 defaults to none, which does not perform reasoning. The supported reasoning values for gpt-5.1 are none, low, medium, and high. Tool calls are supported for all reasoning values in gpt-5.1.
All models before gpt-5.1 default to medium reasoning effort, and do not support none.
The gpt-5-pro model defaults to (and only supports) high reasoning effort.
xhigh is supported for all models after gpt-5.1-codex-max
https://platform.openai.com/docs/changelog clarifies introduction of "minimal" value:
Introduced the minimal reasoning effort value to optimize for fast responses in GPT-5 models (which support reasoning).
https://openai.com/index/gpt-5-1-codex-max clarifies that gpt-5-1-codex-max supports "xhigh":
For non-latency-sensitive tasks, we’re also introducing a new Extra High (‘xhigh’) reasoning effort
And it was the last model released in 5.1 family.
So it looks like all OpenAI reasoning models support low, medium, high.
Models starting with GPT-5 support minimal.
Models starting with gpt-5.1 support none
Models starting with gpt-5.1-codex-max support xhigh (which means gpt-5.1-codex-max and all 5.2, and potentially newer models)
I suggest to match this logic for OpenRouter models in Big-AGI and add support for additional reasoning effort values to respective models.
Where does this happen?
Big-AGI Pro (big-agi.com)
Impact on your workflow
Low - Minor inconvenience
Environment (if applicable)
No response
Additional context
No response
What's happening?
According to https://openrouter.ai/docs/guides/best-practices/reasoning-tokens
So as long as the model support reasoning, presumably all xhigh,high,medium,low,minimal,none values can be used.
I tried xhigh,minimal,none with openai/o1 model on OpenRouter, and they worked, even though official OpenAI API should support only high,medium,low for o1 model, which is an earlier model than gpt-5 (when new values started to be introduced).
https://platform.openai.com/docs/api-reference/responses/create#responses_create-reasoning-effort says the following:
https://platform.openai.com/docs/changelog clarifies introduction of "minimal" value:
https://openai.com/index/gpt-5-1-codex-max clarifies that gpt-5-1-codex-max supports "xhigh":
And it was the last model released in 5.1 family.
So it looks like all OpenAI reasoning models support low, medium, high.
Models starting with GPT-5 support minimal.
Models starting with gpt-5.1 support none
Models starting with gpt-5.1-codex-max support xhigh (which means gpt-5.1-codex-max and all 5.2, and potentially newer models)
I suggest to match this logic for OpenRouter models in Big-AGI and add support for additional reasoning effort values to respective models.
Where does this happen?
Big-AGI Pro (big-agi.com)
Impact on your workflow
Low - Minor inconvenience
Environment (if applicable)
No response
Additional context
No response