Comprehensive side-by-side LLM comparison
Llama 3.1 Nemotron Nano 8B leads with 4.4% higher average benchmark score. Both models have their strengths depending on your specific coding needs.
NVIDIA
Llama-3.1-Nemotron-Nano-8B-v1 is an 8-billion-parameter model from NVIDIA, developed as a fine-tuned variant of Meta's Llama 3.1 8B using NVIDIA's Nemotron post-training methodology, which applies reinforcement learning and process reward modeling to enhance instruction-following and reasoning capability over the base model. The Nano designation marks it as the entry-level member of the Nemotron family, optimized for efficient inference on a single GPU while delivering meaningfully improved performance on instruction alignment and agentic tasks compared to standard Llama 3.1. Released open-weight on HuggingFace, it is designed for deployment in NVIDIA-accelerated environments and supports NVIDIA NIM for enterprise inference.
Alibaba / Qwen
Qwen2-72B-Instruct is a 72-billion-parameter language model released by Alibaba's Qwen team in June 2024, serving as the flagship of the Qwen2 generation and representing a major step in open-weight multilingual modeling. Trained on data spanning 30+ languages with strong coverage of code and structured reasoning, the model was among the first openly released 70B-class models to demonstrate competitive performance across diverse benchmarks. It established the foundation architecture and training methodology that the Qwen2.5 series would later extend.
7 months newer
Qwen2 72B Instruct
Alibaba / Qwen
2024-06-07

Llama 3.1 Nemotron Nano 8B
NVIDIA
2025-01-06
Average performance across 1 common benchmarks
Llama 3.1 Nemotron Nano 8B
Qwen2 72B Instruct
Performance comparison across key benchmark categories
Llama 3.1 Nemotron Nano 8B
Qwen2 72B Instruct
Available providers and their performance metrics
Llama 3.1 Nemotron Nano 8B
Qwen2 72B Instruct
Llama 3.1 Nemotron Nano 8B
Qwen2 72B Instruct