> ## Documentation Index
> Fetch the complete documentation index at: https://documentation.deepmask.io/llms.txt
> Use this file to discover all available pages before exploring further.

# GPT-o3 Mini

> OpenAI's compact reasoning model for STEM and coding tasks. 200K context window, GPQA 79.7%, 0.25s TTFT, 141 tok/s. No image input support.

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.

## 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

<CardGroup cols={2}>
  <Card title="Speed-to-Logic Ratio" icon="bolt">
    Delivers o1-level reasoning at nearly 5x the speed — making deep analytical thinking viable in latency-sensitive pipelines.
  </Card>

  <Card title="Production-Ready Tooling" icon="wrench">
    Native function calling and Structured Outputs support, with near-perfect reliability for API-driven workflows.
  </Card>

  <Card title="FrontierMath Mastery" icon="square-root-variable">
    Solves 32%+ of research-level math problems on the first attempt when paired with Python tool execution.
  </Card>

  <Card title="Competition Coding" icon="trophy">
    Achieves elite Codeforces scores, outperforming previous mini-class reasoning models on algorithmic problem solving.
  </Card>
</CardGroup>

## 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.

<Tip>
  Use the adaptive reasoning effort parameter deliberately: set it to "low" for simple classification or extraction tasks, and reserve "high" for math-heavy or multi-step logic problems. This can cut costs significantly without sacrificing output quality where it counts.
</Tip>

## 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 |

[Try GPT-o3 Mini in DeepMask →](https://chat.deepmask.io/)