GPT-o3 Mini is OpenAI’s compact, cost-efficient reasoning model — built to deliver PhD-level performance in STEM subjects at the speed of a small model. It replaces o1-mini in the 2026 lineup with higher rate limits, better tool integration, and an adaptive reasoning effort parameter that lets you dial in the right balance of speed and depth for each request.Documentation Index
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About GPT-o3 Mini
The o3 series brings chain-of-thought reasoning to a weight class that can run at scale. GPT-o3 Mini achieves o1-level reasoning at nearly 5x the speed, making it practical for production workloads that require real analytical depth — not just pattern matching. It supports function calling and Structured Outputs natively, and achieves an elite Codeforces rating that outperforms previous “mini” reasoning models. Note that image input is not supported.Key Capabilities
Speed-to-Logic Ratio
Delivers o1-level reasoning at nearly 5x the speed — making deep analytical thinking viable in latency-sensitive pipelines.
Production-Ready Tooling
Native function calling and Structured Outputs support, with near-perfect reliability for API-driven workflows.
FrontierMath Mastery
Solves 32%+ of research-level math problems on the first attempt when paired with Python tool execution.
Competition Coding
Achieves elite Codeforces scores, outperforming previous mini-class reasoning models on algorithmic problem solving.
Best For
GPT-o3 Mini is ideal for STEM-intensive tasks where you need genuine reasoning depth but cannot afford the latency or cost of a full frontier model. It is well-suited for real-time tutoring, fast debugging, and structured data extraction. It does not support image inputs — for multi-modal reasoning, use GPT-4o or the GPT-5 series instead.Use Cases
- Real-time tutoring — Instant feedback on complex physics or calculus problems during live sessions.
- Fast debugging — Identifying logic errors in scripts with minimal latency.
- Structured data extraction — Pulling complex variables from messy text into precise JSON via function calling.
Specifications
| Specification | Value |
|---|---|
| Provider | OpenAI |
| Context Window | 200K tokens |
| Reasoning | Adaptive (Low, Medium, High) |
| GPQA Diamond | 79.7% (High effort) |
| Latency (TTFT) | 0.25s |
| Throughput | 141 tokens/sec |
| Image support | No |
| Key use cases | STEM tasks, competitive coding, structured outputs |