Article Summary
NVIDIA has open-sourced its Open Code Reasoning (OCR) model suite, a set of high-performance large language models designed for code reasoning and problem-solving. The OCR models, which include 32B, 14B, and 7B variants, have been released under the Apache 2.0 license. These models have been benchmarked to outperform OpenAI’s o3-Mini and o1 (low) models on the LiveCodeBench benchmark for code reasoning tasks such as debugging, code generation, and logic completion.
What This Means for You
- Access to high-performance code reasoning models: You can now access and use these open-source code reasoning models for your own projects and applications, without the need for proprietary solutions.
- Benefit from NVIDIA’s custom “OCR dataset”: NVIDIA’s OCR dataset, which was used to train the OCR models, has resulted in a 30% improvement in token efficiency, allowing for more accurate code and logical outputs with fewer tokens.
- Choose the right model for your needs: The OCR suite comes in three parameter scales, allowing you to balance scale with performance and choose the right model for your specific use case.
- Integration with popular inference frameworks: The OCR models are compatible with popular inference frameworks, making it easy to plug them into existing code AI infrastructure with minimal overhead.
- Support for open code intelligence: With the release of the OCR suite, NVIDIA is contributing significantly to the growing ecosystem of open code models and empowering the broader AI and developer community to build, fine-tune, and deploy advanced reasoning models in production.
Original Post
NVIDIA continues to push the boundaries of open AI development by open-sourcing its Open Code Reasoning (OCR) model suite — a trio of high-performance large language models purpose-built for code reasoning and problem-solving. The 32B, 14B, and 7B variants, all released under the Apache 2.0 license.
Benchmarked to Beat the Best
The Open Code Reasoning (OCR) models come with notable benchmark achievements, outperforming OpenAI’s o3-Mini and o1 (low) models on the LiveCodeBench benchmark. LiveCodeBench is a comprehensive evaluation suite for code reasoning tasks such as debugging, code generation, and logic completion in real-world developer environments. In direct comparison, NVIDIA’s 32B OCR model tops the leaderboard in reasoning capability for open models.
This leap in performance is attributed not only to model architecture, but to NVIDIA’s custom “OCR dataset” — a high-quality, code-centric training corpus designed to emphasize instruction-following, reasoning, and multi-step code problem solving. According to NVIDIA, this results in a 30% improvement in token efficiency, allowing the models to produce accurate code and logical outputs with fewer tokens.
A Model Lineup for Every Use Case
The Open Code Reasoning suite comes in three parameter scales:
- OpenCodeReasoning-Nemotron-32B
- OpenCodeReasoning-Nemotron-14B
- OpenCodeReasoning-Nemotron-7B
Each model balances scale with performance. The 32B variant delivers state-of-the-art results for high-performance inference and research; the 14B model provides strong reasoning capabilities with reduced compute requirements, and the 7B variant is ideal for resource-constrained environments while retaining competitive performance on benchmarks.
All models are trained using the Nemotron architecture, NVIDIA’s transformer-based backbone optimized for multilingual, multi-task learning. The model weights and configurations are available on Hugging Face.
Compatible with Open Inference Ecosystems
A key feature of these models is out-of-the-box compatibility with popular inference frameworks:
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