Comprehensive side-by-side LLM comparison
. 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
Qwen3-Coder-480B-A35B-Instruct, released by Alibaba's Qwen team on July 22, 2025, is a Mixture-of-Experts large language model with 480 billion total parameters and 35 billion active parameters per inference, specifically designed for agentic coding tasks. It features a 256K token native context window (extendable to 1M tokens with extrapolation) and demonstrated competitive performance on agentic coding, browser automation, and tool-use benchmarks. Qwen3-Coder-480B targets automated software engineering, multi-step code agents, and open-source coding deployments under the Apache 2.0 license.
6 months newer

Llama 3.1 Nemotron Nano 8B
NVIDIA
2025-01-06
Qwen3-Coder-480B
Alibaba / Qwen
2025-07-22
Context window and performance specifications
Available providers and their performance metrics
Llama 3.1 Nemotron Nano 8B
Qwen3-Coder-480B
OpenRouter
Llama 3.1 Nemotron Nano 8B
Qwen3-Coder-480B
Llama 3.1 Nemotron Nano 8B
Qwen3-Coder-480B