Loading the catalogue…
Loading the catalogue…
OpenAI's compute-maximised o3 reasoning model, built for deep multi-step reasoning in math, code, and research over a 200K-token context.
Overview. o3-pro is OpenAI's compute-maximised version of o3, the company's reinforcement-learning-trained reasoning model. It takes text and image input and returns text, with a 200,000-token context window and up to 100,000 tokens of output, served through OpenAI's Responses API. Developers reach for it when a task needs the model to think through a problem at length rather than respond quickly.
Key strengths. o3-pro is trained specifically to reason before answering, which is the defining trait of OpenAI's o-series. Its design points it toward math, code, and research-style tasks that reward depth over speed, and it supports function calling and structured outputs for building reliable, schema-constrained workflows. These are signals from the model's training approach and feature set rather than published benchmark scores, since no independent benchmark results were available at review time.
When to use it. Use o3-pro for complex, high-stakes reasoning work — multi-step math, intricate code generation, or research synthesis — where correctness matters more than response time. It fits batch and asynchronous workflows well, including pipelines that call it through structured outputs or function calling for downstream automation. It is not the right choice for realtime, conversational, or latency-sensitive interactions.
Limitations. o3-pro is served only through the Responses API, not Chat Completions, and does not accept audio input or produce audio output. Its knowledge reflects a mid-2024 training cutoff. OpenAI has announced that the current o3-pro snapshot will retire on December 11, 2026, with GPT-5.6 Sol running in pro reasoning mode as the recommended successor, so teams building on o3-pro today should plan a migration path.
Sovereignty note. o3-pro is a closed-weights model from OpenAI, a US-headquartered creator, so it carries CLOUD Act exposure and cannot be deployed on Sovereign infrastructure. Teams with strict data-residency requirements should weigh this before routing sensitive workloads to it.
| Inference Provider | Sovereignty | Runs in | In / out per 1M | Status | Pin |
|---|---|---|---|---|---|
| L0 | €17.95 / 71.80+1 rate | Active |
14 use cases classified by the curator. The note says why.
Tool-calling + extended-thinking + 200K context support long agentic coding sessions requiring iterative reasoning over large codebases.
Extended-thinking mode favors the deep, deliberate reasoning bug diagnosis requires, especially across large 200K-token codebases.
Native tool-calling support makes this a solid default for structured function-calling applications.
200K context plus extended-thinking supports reasoning over many long documents simultaneously, core to literature review work.
Extended-thinking mode is purpose-built for decomposing and reasoning through multi-step plans; 200K context supports long planning chains.
Extended-thinking reasoning tier is well suited to synthesizing across long, complex source material within a 200K context window.
200K context covers full repositories, but no published code-specific benchmark scores limit confidence beyond acceptable.
Extended-thinking aids multi-step analytical reasoning, though no benchmark data confirms quantitative analysis performance specifically.
200K context window supports long-document question answering, though unpublished benchmarks limit this to acceptable rather than strong.
Extended-thinking mode implies higher latency and cost per response, making it a poor fit for real-time chatbot interactions versus faster-tier models.
No vision/OCR capability documented — not suitable for document OCR workflows.
No vision capability is listed for this model, ruling out image-based Q&A.
No audio capability is listed; this model has no documented speech-to-text support.
Sourced numbers, not our own evaluations. Percentiles are against the whole Stav catalogue.
Computed from aa-intelligence-index, gpqa-diamond.
curl https://api.stav.ai/v1/chat/completions \
-H "Authorization: Bearer $STAV_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "openai/o3-pro",
"reasoning_effort": "high",
"messages": [{"role": "user", "content": "Hello!"}]
}'→ X-Stav-Request-Id · X-Stav-Model · X-Stav-Provider → X-Stav-Sovereignty-Level — the rung the request was served at