Comprehensive side-by-side LLM comparison
Qwen3.5-397B-A17B supports multimodal inputs. Both models have their strengths depending on your specific coding needs.
Moonshot AI
Kimi K2, released by Moonshot AI on July 11, 2025, is an open-weight Mixture-of-Experts large language model with 1 trillion total parameters and 32 billion active parameters per inference. It features a 256K token context window (expanded from 128K in an September 2025 update) and demonstrated strong performance on agentic coding benchmarks. Kimi K2 targets software engineering agents, tool-use workflows, and open-source deployments under a modified MIT license.
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.
7 months newer
Kimi K2
Moonshot AI
2025-07-11
Qwen3.5-397B-A17B
Alibaba / Qwen
2026-02-16
Context window and performance specifications
Available providers and their performance metrics
Kimi K2
Moonshot AI
Qwen3.5-397B-A17B
Kimi K2
Qwen3.5-397B-A17B
Kimi K2
Qwen3.5-397B-A17B