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.
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.
| o3 | Llama 3.3 70B Instruct | |
|---|---|---|
Provider | ||
Web Site | ||
Release Date | Apr 16, 2025 1 week ago | Dec 06, 2024 4 months ago |
Modalities | text images | text |
API Providers | OpenAI API | Fireworks, Together, DeepInfra, Hyperbolic |
Knowledge Cut-off Date | - | 12.2024 |
Open Source | No | Yes |
Pricing Input | $10.00 per million tokens | $0.23 per million tokens |
Pricing Output | $40.00 per million tokens | $0.40 per million tokens |
MMLU | 82.9% Source | 86% 0-shot, CoT Source |
MMLU Pro | - | 68.9% 5-shot, CoT Source |
MMMU | - | Not available |
HellaSwag | - | Not available |
HumanEval | - | 88.4% pass@1 Source |
MATH | - | 77% 0-shot, CoT Source |
GPQA | 83.3% Diamond, no tools Source | 50.5% 0-shot, CoT Source |
IFEval | - | 92.1% Source |
Mobile Application | - |
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