Comprehensive side-by-side LLM comparison
Both models show comparable benchmark performance. 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
Qwen2.5-32B-Instruct is a 32-billion-parameter open-weight model from Alibaba's Qwen team, released in September 2024 as part of the Qwen2.5 series trained on 18 trillion tokens. The model is positioned as a high-capability option for developers with access to multi-GPU setups or high-VRAM hardware, offering strong performance on coding, structured reasoning, and multilingual tasks while remaining fully open under Apache 2.0. Its 128K context window and support for structured output generation made it a popular choice for document processing and agentic workflows in the open-source community.
3 months newer
Qwen2.5 32B Instruct
Alibaba / Qwen
2024-09-19

Llama 3.1 Nemotron Nano 8B
NVIDIA
2025-01-06
Average performance across 1 common benchmarks
Llama 3.1 Nemotron Nano 8B
Qwen2.5 32B Instruct
Performance comparison across key benchmark categories
Llama 3.1 Nemotron Nano 8B
Qwen2.5 32B Instruct
Available providers and their performance metrics
Llama 3.1 Nemotron Nano 8B
Qwen2.5 32B Instruct
Llama 3.1 Nemotron Nano 8B
Qwen2.5 32B Instruct