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.
Llama 3.3 70B Instruct, created by Meta, is a multilingual large language model specifically fine-tuned for instruction-based tasks and optimized for conversational applications. It is capable of processing and generating text in multiple languages, with a context window supporting up to 128,000 tokens. Launched on December 6, 2024, the model surpasses numerous open-source and proprietary chat models in various industry benchmarks. It utilizes Grouped-Query Attention (GQA) to improve scalability and has been trained on a diverse dataset comprising over 15 trillion tokens from publicly available sources. The model's knowledge is current up to December 2023.
| o4-mini | Llama 3.3 70B Instruct | |
|---|---|---|
Web Site
| ||
Provider
| ||
Chat
| ||
Release Date
| ||
Modalities
| text images | text |
API Providers
| OpenAI API | Fireworks, Together, DeepInfra, Hyperbolic |
Knowledge Cut-off Date
| - | 12.2024 |
Open Source
| No | Yes |
Pricing Input
| $1.10 per million tokens | $0.23 per million tokens |
Pricing Output
| $4.40 per million tokens | $0.40 per million tokens |
MMLU
| fort | 86% 0-shot, CoT Source |
MMLU-Pro
| - | 68.9% 5-shot, CoT Source |
MMMU
| 81.6% Source | Not available |
HellaSwag
| - | Not available |
HumanEval
| 14.28% Source | 88.4% pass@1 Source |
MATH
| - | 77% 0-shot, CoT Source |
GPQA
| 81.4% Source | 50.5% 0-shot, CoT Source |
IFEval
| - | 92.1% Source |
SimpleQA
| - | - |
AIME 2024 | 93.4% Source | - |
AIME 2025 | 92.7% Source | - |
Aider Polyglot
| - | - |
LiveCodeBench v5
| - | - |
Global MMLU (Lite)
| - | - |
MathVista
| - | - |
Mobile Application | - |
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