# o3-mini ![Organization](https://img.shields.io/badge/Org-OpenAI-74aa9c) ![Context](https://img.shields.io/badge/Context-200K%20tokens-1f6feb) ![License](https://img.shields.io/badge/License-Proprietary-d29922) ![Released](https://img.shields.io/badge/Released-Jan%202025-555) ![Reasoning](https://img.shields.io/badge/Type-Reasoning-8250df) > **Quick answer:** o3-mini is OpenAI's January 2025 compact reasoning model, scoring 97.9% on MATH, 86.9% on MMLU, and 15.0% on SimpleQA. It brings o3-class mathematical and scientific reasoning at a lower cost than full o3, with three effort levels: low, medium, and high. ## At a Glance **Where o3-mini leads** - 97.9% MATH — near-perfect competition mathematics performance - 86.9% MMLU — strong general knowledge for a reasoning-focused model - Cost-efficient reasoning: lower price than o3 and o4-mini - Three reasoning effort levels (low/medium/high) for cost-quality tradeoff - Strong STEM performance: coding, mathematics, science **Where it lags** - 15.0% SimpleQA — reasoning models sacrifice factual recall for reasoning depth - Text-only: no vision capability - Higher latency than GPT-4o-mini due to reasoning steps - Superseded by o4-mini for most reasoning tasks with added vision support **Best for:** Cost-sensitive reasoning tasks; STEM problem solving; API integration requiring reasoning at low cost; teams evaluating o-series models. ## What o3-mini Is o3-mini (released January 31, 2025) is OpenAI's small reasoning model in the o3 family. Like o1 and o3, it uses extended chain-of-thought reasoning before responding — trading latency for accuracy on complex reasoning tasks. Its 97.9% MATH score represents near-ceiling performance on competition mathematics. The model was designed for developers who need o3-class reasoning quality but are cost-constrained by full o3 pricing. Three effort levels (low/medium/high) allow tuning the latency-cost-accuracy tradeoff. o4-mini (April 2025) superseded o3-mini with better scores across all benchmarks, added vision capability, and similar pricing. For new deployments, o4-mini is the recommended choice unless o3-mini has been specifically validated. ## Specifications | Field | Value | |---|---| | Organization | OpenAI | | Context window | 200,000 tokens | | License | Proprietary (API only) | | Release date | January 31, 2025 | | Knowledge cutoff | September 2024 | | Modality | Text only | | Reasoning effort | Low / Medium / High | ## Pricing | Tier | Price | |---|---| | Input | $1.10 / 1M tokens | | Output | $4.40 / 1M tokens | Pricing from the OpenAI API pricing page. Cached input tokens are discounted. ## Context Window o3-mini supports a **200,000-token context window** — approximately **150 pages** of text. ## Public Benchmark Scores | Benchmark | Score | Source | Date | |---|---|---|---| | [MATH](/benchmarks/math) | 97.9% | Benchgen evaluation | 2025-07 | | [MMLU](/benchmarks/mmlu) | 86.9% | Benchgen evaluation | 2025-07 | | [SimpleQA](/benchmarks/simpleqa) | 15.0% | Benchgen evaluation | 2025-07 | ## o3-mini vs Alternatives | Model | MATH | MMLU | Context | Price (in/out) | |---|---|---|---|---| | **o3-mini** | 97.9% | 86.9% | 200K | $1.10/$4.40 | | [o4-mini](/models/openai/o4-mini) | — | — | 200K | $1.10/$4.40 | | [o1-preview](/models/openai/o1-preview) | 85.5% | 90.8% | 128K | — | | [o4-mini-high](/models/openai/o4-mini-high) | — | — | 200K | $1.10/$4.40 | o3-mini vs o4-mini: o4-mini achieves better benchmark scores with added multimodal capability at identical pricing. For new deployments, o4-mini is recommended. ## Related Models & Benchmarks - Compare: [o4-mini](/models/openai/o4-mini) · [o4-mini-high](/models/openai/o4-mini-high) · [o1-preview](/models/openai/o1-preview) - Benchmarks: [MATH](/benchmarks/math) · [MMLU](/benchmarks/mmlu) · [SimpleQA](/benchmarks/simpleqa) ## Frequently Asked Questions

What is o3-mini?

o3-mini is OpenAI's January 2025 compact reasoning model, scoring 97.9% MATH and 86.9% MMLU. It brings o3-class reasoning at lower cost with three effort levels.

What is o3-mini's context window?

o3-mini supports a 200,000-token context window.

Should I use o3-mini or o4-mini?

o4-mini for new deployments: better benchmarks, multimodal capability, and same pricing ($1.10/$4.40 per 1M tokens). o3-mini is appropriate for existing integrations.

What is the difference between o3-mini low, medium, and high?

Effort levels control how much reasoning the model performs. Low: faster, cheaper, lower accuracy. High: slower, more expensive, highest accuracy. Medium is a balanced default.

--- *Specs from OpenAI's o3-mini launch (January 2025) and Benchgen evaluations. Last updated 2026-07-24.*

o3-mini — Files