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OpenAI's higher-compute o3 reasoning model for deep, batch-style math, code, and research tasks via the Responses API.
Overview. o3-pro-2025-06-10 is OpenAI's higher-compute version of o3, a closed-weight reasoning model built to think through problems with reinforcement-learned reasoning tokens before producing an answer. It takes text and image input, returns text output, and supports a 200K context window with up to 100K output tokens. It's served exclusively through OpenAI's Responses API, with no Chat Completions route.
Key strengths. As a higher-compute o3 variant, it's built for depth over speed: multi-step math, code reasoning, and research-style questions where OpenAI's reasoning-token approach pays off. Benchmark signal (strong AIME math and GPQA Diamond science scores, plus top-tier Codeforces coding results) points to its clearest advantage in math, science, and code reasoning, with comparatively less distinctive benefit for open-ended writing or conversational tasks where reasoning depth matters less.
When to use it. Reach for this model when a task benefits from extended reasoning and you can tolerate longer turnaround — proof-style math, multi-step code analysis, or research synthesis run well in batch or asynchronous workflows rather than interactive chat. It's not built for realtime or latency-sensitive use; its Responses-API-only, Batch-friendly design signals long-running calls rather than conversational back-and-forth.
Limitations. The Responses-only API means it won't drop into Chat Completions-based integrations, and its batch-oriented profile makes it a poor fit for interactive or latency-sensitive use. Its knowledge cutoff is mid-2024, and OpenAI has marked this snapshot deprecated, with removal scheduled for December 2026 in favor of gpt-5.6-sol running in a comparable reasoning mode. OpenAI has not disclosed its parameter count or architecture.
Sovereignty note. o3-pro is built and hosted by OpenAI, a US company, so it carries CLOUD Act exposure and runs on closed weights with no Sovereign deployment option — workloads with strict data-residency requirements should look to EU-based or open-weights alternatives instead.
| Inference Provider | Sovereignty | Runs in | In / out per 1M | Status | Pin |
|---|---|---|---|---|---|
| L0 | €17.95 / 71.80+1 rate | Active |
15 use cases classified by the curator. The note says why.
Extended-thinking mode is well suited to the multi-step causal reasoning bug diagnosis requires, backed by a 200K context window for large codebases.
200K context window allows ingesting full documents for Q&A, and extended-thinking supports careful multi-hop answer derivation.
Tool-calling is a documented capability, directly matching function-calling application requirements.
Extended-thinking mode plus tool-calling and a 200K context window are the core requirements for decomposing and tracking long multi-step agentic plans.
Extended-thinking plus 200K context window is a direct architectural fit for synthesizing findings across many long source documents.
Native tool-calling support with extended-thinking reasoning makes this a strong fit for coordinating multiple tool invocations in sequence.
Tool-calling and extended-thinking support agentic coding workflows, but no published SWE-Bench or coding-specific benchmark means confidence is capped at acceptable pending validation.
200K context window supports full-repository review, though no code-specific benchmark is published to confirm strong-tier performance.
Extended-thinking mode aids structured analytical reasoning, though lack of published quantitative benchmarks limits confidence to acceptable.
200K context window accommodates large bodies of literature for review, consistent with the family's long-context design.
200K context window supports maintaining coherence across long documents, though no writing-specific benchmark is published.
No vision capability is listed, making it unsuitable for extracting data from chart images.
No vision/OCR capability is documented, so this model cannot process scanned or image-based documents.
No vision capability is listed for this model, ruling it out for image-based question answering.
No vision capability is documented, precluding screenshot interpretation tasks.
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-2025-06-10",
"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