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.
Over the past five months since the release of Qwen2-VL, developers have built new models based on it, contributing valuable feedback. Now, Qwen2.5-VL introduces enhanced capabilities, including precise analysis of images, text, and charts, as well as object localization with structured JSON outputs. It understands long videos, identifies key events, and functions as an agent, interacting with tools on computers and phones. The model's architecture features dynamic video processing and an optimized ViT encoder for improved speed and accuracy.
| o4-mini | Qwen2.5-VL-32B | |
|---|---|---|
Web Site
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Provider
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Chat
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Release Date
| ||
Modalities
| text images | text images video |
API Providers
| OpenAI API | - |
Knowledge Cut-off Date
| - | Unknown |
Open Source
| No | Yes (Source) |
Pricing Input
| $1.10 per million tokens | $0 |
Pricing Output
| $4.40 per million tokens | $0 |
MMLU
| fort | 78.4% Source |
MMLU-Pro
| - | 49.5% |
MMMU
| 81.6% Source | 70% |
HellaSwag
| - | Not available |
HumanEval
| 14.28% Source | Not available |
MATH
| - | 82.2% |
GPQA
| 81.4% Source | 46.0% Diamond |
IFEval
| - | Not available |
SimpleQA
| - | - |
AIME 2024 | 93.4% Source | - |
AIME 2025 | 92.7% Source | - |
Aider Polyglot
| - | - |
LiveCodeBench v5
| - | - |
Global MMLU (Lite)
| - | - |
MathVista
| - | - |
Mobile Application | - | |
MathArena | ||
| Avg. Score | 87% | - |
AIME 2025 A test based on problems from the American Invitational Mathematics Examination, designed to assess the mathematical skills of models. | 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. | 83% | - |
BRUMO 2025 | 87% | - |
SMT 2025 A test based on problems from the Stanford Math Tournament, 2025, designed to assess the mathematical skills of models. | 89% | - |
CMIMC 2025 A test based on problems from the Canadian Mathematical Olympiad, 2025, designed to assess the mathematical skills of models. | 84% | - |
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