glm

GLM-5.2 is a new model comparable to Opus 4.8, featuring 1M context, new IS attention, improved speculative decoding, and flexible thinking-effort levels. It is released under MIT license with day-0 support in transformers, vLLM, and SGLang.

Zhipu AI (zai_org) has open-sourced GLM 5.2, a flagship model for coding and long-horizon agentic tasks with a usable 1M-token context. Modular is a Day Zero launch partner, offering optimized serving on Modular Cloud.

Nathan Lambert and Finbarr Timbers discuss the latest post-training recipes for large language models, including DeepSeek V4, GLM 5.1, Kimi K2.6, and the industry shift to multi-teacher on-policy distillation.

A user shares their experience running large LLMs on a 3x3090 (72GB VRAM) setup in Q2 2026, recommending models like GPT-OSS 120b, Qwen3.5 122b, and GLM Air 4.5 106B, and asking for newer alternatives.

Stepfun 3.7 Flash is a compact vision model that achieves aesthetics close to GLM 5.1 and 80% of its 3D world understanding, while using only 25% of the parameters, making it highly RAM-efficient.

A tweet highlights that 20 free daily tokens from Tembo provide full access to GPT 5.5 and GLM, and Opus 4.7 handles architecture tasks at zero cost, matching paid tools' output.

According to the arena leaderboard, open weights models GLM and Mimo outperform Gemini 3.5 Flash in coding benchmarks.

This article tests four open-source Chinese AI models — Zhipu GLM 5.1, Moonshot Kimi K2.6, Stepfun MIMO 2.5 Pro, and DeepSeek V4 Pro — on programming tasks. It finds that GLM leads overall in most tasks but not absolutely; each model has its own strengths and weaknesses.

The author conducted a comparative evaluation of four domestic AI models: DeepSeek V4, Kimi K2.6, GLM-5.1, and MiniMax M2.7. The analysis covers their strengths and weaknesses regarding cost, long-context processing, coding stability, and reasoning performance, offering specific recommendations on how to route tasks involving large document analysis, long-running background jobs, and bulk content generation.

A new 18B merged quantized model, Qwopus-GLM-18B-GGUF, outperforms 35B MoE models while using half the VRAM and running on consumer GPUs.