OpenAI o3 is the most advanced reasoning model from OpenAI, purpose-built for handling complex, high-cognition tasks. Launched in April 2025, it delivers exceptional performance in software engineering, mathematics, and scientific problem-solving. The model introduces three levels of reasoning effort—low, medium, and high—allowing users to balance between latency and depth of reasoning based on task complexity. o3 supports essential tools for developers, including function calling, structured outputs, and system-level messaging. With built-in vision capabilities, o3 can interpret and analyze images, making it suitable for multimodal applications. It’s available through Chat Completions API, Assistants API, and Batch API for flexible integration into enterprise and research workflows.
OpenAI o4-mini is the newest lightweight model in the o-series, engineered for efficient and capable reasoning across text and visual tasks. Optimized for speed and performance, it excels in code generation and image-based understanding, while maintaining a balance between latency and reasoning depth. The model supports a 200,000-token context window with up to 100,000 output tokens, making it suitable for extended, high-volume interactions. It handles both text and image inputs, producing textual outputs with advanced reasoning capabilities. With its compact architecture and versatile performance, o4-mini is ideal for a wide array of real-world applications demanding fast, cost-effective intelligence.
| o3 | o4-mini | |
|---|---|---|
Web Site
| ||
Provider
| ||
Chat
| ||
Release Date
| ||
Modalities
| text images | text images |
API Providers
| OpenAI API | OpenAI API |
Knowledge Cut-off Date
| - | - |
Open Source
| No | No |
Pricing Input
| $10.00 per million tokens | $1.10 per million tokens |
Pricing Output
| $40.00 per million tokens | $4.40 per million tokens |
MMLU
| 82.9% Source | fort |
MMLU-Pro
| - | - |
MMMU
| - | 81.6% Source |
HellaSwag
| - | - |
HumanEval
| - | 14.28% Source |
MATH
| - | - |
GPQA
| 83.3% Diamond, no tools Source | 81.4% Source |
IFEval
| - | - |
SimpleQA
| - | - |
AIME 2024 | 91.6% Source | 93.4% Source |
AIME 2025 | 88.9% Source | 92.7% Source |
Aider Polyglot
| - | - |
LiveCodeBench v5
| - | - |
Global MMLU (Lite)
| - | - |
MathVista
| - | - |
Mobile Application | ||
MathArena | ||
| Avg. Score | 86% | 87% |
AIME 2025 A test based on problems from the American Invitational Mathematics Examination, designed to assess the mathematical skills of models. | 89% | 92% |
HMMT February 2025 A test based on problems from the Harvard-MIT Mathematics Tournament, February 2025, designed to assess the mathematical skills of models. | 78% | 83% |
BRUMO 2025 | 96% | 87% |
SMT 2025 A test based on problems from the Stanford Math Tournament, 2025, designed to assess the mathematical skills of models. | 88% | 89% |
CMIMC 2025 A test based on problems from the Canadian Mathematical Olympiad, 2025, designed to assess the mathematical skills of models. | 78% | 84% |
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