Comprehensive side-by-side LLM comparison
Qwen3.5-397B-A17B supports multimodal inputs. Both models have their strengths depending on your specific coding needs.
Alibaba / Qwen
Qwen2.5-Coder-32B-Instruct is a 32-billion-parameter code-specialized model from Alibaba, released in November 2024 and trained on a large corpus spanning 92 programming languages including C, Python, Java, Rust, and domain-specific languages. The model was designed to provide competitive code generation, repair, and reasoning capabilities as an open-weight alternative for developers building code assistant tools and automated review pipelines. Its 128K context window enables whole-file and multi-file code comprehension, making it particularly suited for complex repository-level tasks.
Alibaba / Qwen
Qwen3.5-397B-A17B is a 397-billion-parameter mixture-of-experts model from Alibaba's Qwen team, released in February 2026 as the open-weight flagship of the Qwen3.5 series, featuring 17 billion active parameters per forward pass through a hybrid linear-attention and sparse-MoE architecture based on Gated Delta Networks. The model was co-trained on text, images, and video using early fusion, making it natively multimodal across a 262K token context window, while achieving significantly higher inference throughput than comparable dense models due to its sparse computation design. At release it was one of the most capable open-weight models publicly available, offered under Apache 2.0 and accessible through Alibaba's DashScope API as the Qwen3.5-Plus endpoint.
1 year newer
Qwen2.5-Coder 32B Instruct
Alibaba / Qwen
2024-11-12
Qwen3.5-397B-A17B
Alibaba / Qwen
2026-02-16
Available providers and their performance metrics
Qwen2.5-Coder 32B Instruct
Qwen3.5-397B-A17B
Qwen2.5-Coder 32B Instruct
Qwen3.5-397B-A17B