GLM-5.1 vs Qwen3 Family

GLM-5.1 is Zhipu AI's latest open-weights language model — a 744B parameter mixture-of-experts (MoE) architecture that activates 40B parameters per forward pass. Released under the MIT license with a 200,000-token context window, it has quietly topped the SWE-Bench Pro leaderboard, surpassing both Claude Opus 4.6 and GPT-5.4 on expert-level software engineering tasks. The MoE architecture means GLM-5.1 is significantly cheaper to run per token than a dense 744B model, with inference costs approaching dense 40B models for most workloads. Zhipu AI (a Tsinghua University spin-out) has steadily iterated on the GLM family to produce a text-focused reasoning model that holds its own against proprietary frontier models — now, for the first time, reportedly exceeding them on coding benchmarks. The MIT license is the headline for enterprise and research users: full commercial use, no usage restrictions, no API dependency. This puts GLM-5.1 in direct competition with Qwen3.5 for the "best open-weights model you can actually use for anything" crown, with a differentiating edge in software engineering tasks specifically.

Alibaba's Qwen team released the full Qwen3 model family this week — 8 models ranging from 0.6B to 235B parameters, spanning both dense and Mixture-of-Experts (MoE) architectures. The headline model is Qwen3-235B-A22B, a 235B MoE that activates 22B parameters per token and matches GPT-4.1 on coding and math benchmarks while running at a fraction of the cost. All Qwen3 models feature switchable "thinking modes" — a built-in chain-of-thought toggle that can be enabled or disabled per request. This eliminates the need for separate reasoning vs. instruct variants, letting developers trade latency for accuracy dynamically. All models are released under Apache 2.0, with weights available on Hugging Face and ModelScope. The smaller models are competitive at their size class: Qwen3-4B reportedly matches Qwen2.5-72B-Instruct on several benchmarks, and the 0.6B model is designed to run efficiently on embedded and edge devices. The release also introduces a new multilingual benchmark covering 119 languages, on which the Qwen3 family sets new state-of-the-art scores for open-weights models.