> ## Documentation Index > Fetch the complete documentation index at: https://docs.orq.ai/llms.txt > Use this file to discover all available pages before exploring further. # Reasoning models > Use reasoning and thinking-capable models like o1, o3, and Claude through the AI Gateway. Configure reasoning effort and token budgets per request. Reasoning and thinking models perform internal deliberation before generating a response. Each provider exposes this differently: OpenAI uses `reasoning_effort`, while Google Gemini and Anthropic use a `thinking` object. The **AI Gateway** accepts all three controls and normalizes values to what each model actually supports before forwarding the request. | Provider | Control | Values | | ----------------------- | ------------------------- | --------------------------------------------------- | | OpenAI o-series | `reasoning_effort` | `none`, `minimal`, `low`, `medium`, `high`, `xhigh` | | Google Gemini 3 preview | `thinking.thinking_level` | `low`, `high` | | Google Gemini 2.5 | `thinking.budget_tokens` | integer | | Anthropic Claude | `thinking.budget_tokens` | integer | **Use Cases** * Problems requiring multi-step logical deduction (math proofs, code debugging, planning). * Complex analysis where a standard model produces shallow or incorrect results. * Research tasks where depth of reasoning matters more than response speed. * Benchmarking reasoning quality across providers on identical prompts. *** ## Quick Start The router supports three reasoning controls: * `reasoning` object on `POST /responses` for OpenAI reasoning models. * `reasoning_effort` on `POST /chat/completions` for OpenAI reasoning models. * `thinking` on `POST /chat/completions` for Google Gemini and Anthropic extended thinking. ```bash cURL theme={"theme":{"light":"github-light","dark":"github-dark"}} curl -X POST https://api.orq.ai/v3/router/responses \ -H "Authorization: Bearer $ORQ_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "openai/o3-mini", "input": "Analyze the logical flaw in this argument.", "reasoning": {"effort": "medium"} }' ``` ```typescript TypeScript theme={"theme":{"light":"github-light","dark":"github-dark"}} import OpenAI from 'openai'; const client = new OpenAI({ apiKey: process.env.ORQ_API_KEY, baseURL: 'https://api.orq.ai/v3/router', }); const response = await client.responses.create({ model: 'openai/o3-mini', input: 'Analyze the logical flaw in this argument.', reasoning: { effort: 'medium' }, }); console.log(response.output_text); ``` ```python Python theme={"theme":{"light":"github-light","dark":"github-dark"}} from openai import OpenAI import os client = OpenAI( api_key=os.environ.get("ORQ_API_KEY"), base_url="https://api.orq.ai/v3/router", ) response = client.responses.create( model="openai/o3-mini", input="Analyze the logical flaw in this argument.", extra_body={"reasoning": {"effort": "medium"}}, ) print(response.output_text) ``` ```typescript TypeScript (Chat Completions) theme={"theme":{"light":"github-light","dark":"github-dark"}} import OpenAI from 'openai'; const client = new OpenAI({ apiKey: process.env.ORQ_API_KEY, baseURL: 'https://api.orq.ai/v3/router', }); const response = await client.chat.completions.create({ model: 'openai/o3-mini', messages: [{ role: 'user', content: 'Analyze the logical flaw in this argument.' }], reasoning_effort: 'medium', }); console.log(response.choices[0].message.content ?? ""); const geminiLevel = await client.chat.completions.create({ model: 'google/gemini-2.5-pro', messages: [{ role: 'user', content: 'Plan a 3-day Tokyo itinerary under $500.' }], thinking: { type: 'enabled', budget_tokens: 4096 }, }); console.log(geminiLevel.choices[0].message.content ?? ""); const anthropicThinking = await client.chat.completions.create({ model: 'anthropic/claude-sonnet-4-6', messages: [{ role: 'user', content: 'Design a rate limiting strategy for a global API.' }], thinking: { type: 'enabled', budget_tokens: 4096 }, max_tokens: 8000, }); console.log(anthropicThinking.choices[0].message.content ?? ""); ``` The Go gateway mirrors the same contract internally through `models.ModelParameters.ReasoningEffort` and `models.ModelParameters.Thinking`. ## Request Fields | Field | Type | Values | Notes | | ------------------------- | ------ | --------------------------------------------------- | -------------------------------------------------- | | `reasoning_effort` | string | `none`, `minimal`, `low`, `medium`, `high`, `xhigh` | OpenAI-style reasoning control | | `thinking.type` | string | `enabled`, `disabled` | Used by Google Gemini and Anthropic thinking paths | | `thinking.budget_tokens` | number | integer | Budget-based thinking | | `thinking.thinking_level` | string | `low`, `high` | Level-based thinking for Gemini 3 preview models | Treat `thinking.budget_tokens` and `thinking.thinking_level` as mutually exclusive. On the Google path, if `thinking_level` is present it takes precedence over `budget_tokens`. ## Provider Behavior ### OpenAI reasoning models Use `reasoning_effort` on `POST /chat/completions`. Current registry examples: * `openai/o1`. * `openai/o1-pro`. * `openai/o3-mini`. * `openai/o3`. * `openai/o3-pro`. The router schema accepts all six enum values, but model support is ultimately model-specific. The router normalizes `reasoning_effort` to the nearest value a model supports before forwarding the request. For example, `openai/gpt-5.4` does not support `xhigh`: the router maps it to `high`. Models that do support `xhigh` receive the value as-is. When `reasoning_effort` is set, the router automatically drops `temperature` and `top_p` before forwarding the request. These parameters are incompatible with OpenAI reasoning models and will cause an error if sent directly. Set up your OpenAI API key and explore all supported models including the o1 and o3 families. ### Google Gemini Use the `thinking` object. Level-based (`thinking_level`) examples: * `google/gemini-3-flash-preview`. * `google/gemini-3-pro-preview`. Budget-based (`budget_tokens`) examples: * `google/gemini-2.5-flash`. * `google/gemini-2.5-flash-lite`. * `google/gemini-2.5-pro`. Router behavior: * `thinking: { "type": "disabled" }` is valid * On `thinking_enforced` models such as `google/gemini-2.5-pro`, disabling thinking is coerced to a minimum budget of `128` * On non-enforced Gemini models, disabling thinking becomes a budget of `0` Set up your Google AI API key and explore Gemini 2.5 and Gemini 3 thinking models. ### Anthropic Claude On `POST /chat/completions`, Anthropic uses `thinking: { type, budget_tokens }`. Current registry examples: * `anthropic/claude-sonnet-4-20250514`. * `anthropic/claude-sonnet-4-5-20250929`. * `anthropic/claude-opus-4-5-20251101`. Router behavior: * Anthropic chat completions only forward thinking when `type` is `enabled`. * `budget_tokens` must be greater than `0` to be forwarded. * `thinking_level` is not used for Anthropic chat completions. Set up your Anthropic API key and explore Claude extended thinking capabilities. ## Responses API `POST /responses` supports reasoning for **OpenAI models only**. Use the OpenAI-style `reasoning` object with `effort` instead of `reasoning_effort`. `thinking` (Anthropic and Google Gemini) is not supported on the `/responses` endpoint. Use `POST /chat/completions` for Anthropic and Google reasoning models. ```json JSON theme={"theme":{"light":"github-light","dark":"github-dark"}} { "model": "openai/o3-mini", "input": "Solve this step by step.", "reasoning": { "effort": "medium" } } ``` ## Usage and Output Reasoning token usage is returned under `usage.completion_tokens_details.reasoning_tokens`. ```json JSON theme={"theme":{"light":"github-light","dark":"github-dark"}} { "usage": { "prompt_tokens": 120, "completion_tokens": 980, "total_tokens": 1100, "completion_tokens_details": { "reasoning_tokens": 640 } } } ``` Do not rely on visible chain-of-thought text being present in every response. The stable contract is the request fields above plus token usage. Provider-specific fields such as `reasoning`, `reasoning_signature`, or `redacted_reasoning` may appear, but they are optional. ## Code Examples ```bash cURL theme={"theme":{"light":"github-light","dark":"github-dark"}} curl -X POST https://api.orq.ai/v3/router/responses \ -H "Authorization: Bearer $ORQ_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "openai/o3-mini", "input": "Solve this step by step: What is 15% of 250?", "reasoning": {"effort": "medium"} }' ``` ```bash cURL (Chat Completions) theme={"theme":{"light":"github-light","dark":"github-dark"}} curl -X POST https://api.orq.ai/v3/router/chat/completions \ -H "Authorization: Bearer $ORQ_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "anthropic/claude-opus-4-5-20251101", "messages": [ { "role": "user", "content": "Break down the tradeoffs of event-driven vs request-response systems." } ], "thinking": { "type": "enabled", "budget_tokens": 8192 }, "max_tokens": 16000 }' ``` ```typescript TypeScript theme={"theme":{"light":"github-light","dark":"github-dark"}} import OpenAI from 'openai'; const client = new OpenAI({ apiKey: process.env.ORQ_API_KEY, baseURL: 'https://api.orq.ai/v3/router', }); const response = await client.responses.create({ model: 'openai/o3-mini', input: 'Solve this step by step: What is 15% of 250?', reasoning: { effort: 'medium' }, }); console.log(response.output_text); ``` ```python Python theme={"theme":{"light":"github-light","dark":"github-dark"}} from openai import OpenAI import os client = OpenAI( api_key=os.environ.get("ORQ_API_KEY"), base_url="https://api.orq.ai/v3/router", ) response = client.responses.create( model="openai/o3-mini", input="Solve this step by step: What is 15% of 250?", extra_body={"reasoning": {"effort": "medium"}}, ) print(response.output_text) ``` ```typescript TypeScript (Chat Completions: Anthropic thinking) theme={"theme":{"light":"github-light","dark":"github-dark"}} import OpenAI from 'openai'; const client = new OpenAI({ apiKey: process.env.ORQ_API_KEY, baseURL: 'https://api.orq.ai/v3/router', }); const response = await client.chat.completions.create({ model: 'anthropic/claude-opus-4-5-20251101', messages: [ { role: 'user', content: 'Break down the tradeoffs of event-driven vs request-response systems.', }, ], thinking: { type: 'enabled', budget_tokens: 8192 }, max_tokens: 16000, }); console.log(response.choices[0].message.content ?? ""); ``` ```python Python (Chat Completions: Anthropic thinking) theme={"theme":{"light":"github-light","dark":"github-dark"}} from openai import OpenAI import os client = OpenAI( api_key=os.environ.get("ORQ_API_KEY"), base_url="https://api.orq.ai/v3/router", ) response = client.chat.completions.create( model="anthropic/claude-opus-4-5-20251101", messages=[ { "role": "user", "content": "Break down the tradeoffs of event-driven vs request-response systems.", } ], max_tokens=16000, extra_body={ "thinking": { "type": "enabled", "budget_tokens": 8192, } }, ) print(response.choices[0].message.content or "") ``` ## Choosing a Setting Use `reasoning_effort` when the model is in the OpenAI `o1` or `o3` family. Use `thinking_level` for Gemini 3 preview models. Use `budget_tokens` for Anthropic and budget-based Gemini models. If you need the current model catalog, use [Supported Models](/docs/ai-studio/ai-gateway/supported-models).