Alibaba Qwen3.5

Alibaba has released Qwen3.5, headlined by a 397 billion parameter Mixture-of-Experts model with 17 billion active parameters per token. Shipped under Apache 2.0 on Hugging Face, Qwen3.5 scores 93.3% on AIME 2026, 85.0 on LiveCodeBench v6, and 76.8% on SWE-Bench Verified, putting it in frontier territory for math, coding, and agent tasks. The broader Qwen3.5 family spans dense models from sub-1 billion up to 32 billion parameters, plus sparse MoE variants, giving developers open-weight options at every scale.

Moonshot Kimi K2.5

Moonshot AI has released Kimi K2.5, a 1 trillion parameter Mixture-of-Experts language model with 32 billion active parameters per request. The model uses 61 layers with 384 experts and sparse 8-expert activation, natively trained on roughly 15 trillion mixed vision and text tokens for a 256K context window. Kimi K2.5 outperforms GPT-5.2 on MMMU Pro (78.5%), BrowseComp (74.9%), and AIME 2025 (96.1%), and the Agent Swarm configuration reaches 50.2% on Humanity Last Exam at 76% lower cost than Claude Opus 4.5. Weights ship on Hugging Face under a Modified MIT license.

Z.ai GLM-5.1

Z.ai (formerly Zhipu AI) has released GLM-5.1, an open-weight 744 billion parameter Mixture-of-Experts model with 40 billion active parameters. The model immediately took the top spot on SWE-Bench Pro with 58.4, beating GPT-5.4 (57.7) and Claude Opus 4.6 (57.3). Built on DeepSeek Sparse Attention with a 200K context window and 131K maximum output, GLM-5.1 is engineered for sustained autonomous coding agents capable of running plan, execute, test, fix, optimize loops for up to eight hours. The weights are available on Hugging Face under a permissive MIT license for full commercial use.