Zhipu GLM

The author benchmarks GPT-5.5, DeepSeek V4 Pro, and GLM-5.2 on the AA-Omniscience hallucination metric and a Python async coding prompt. GPT-5.5 hits 86% hallucination, DeepSeek V4 Pro 94%, while GLM-5.2 scores 28%. DeepSeek V4 Pro spent nearly 4 minutes and 7.7k reasoning tokens producing a confidently wrong solution; GLM-5.2 needed 12 seconds and ~800 tokens to flag the prompt as technically impossible under single-threaded, no-polling constraints. GLM-5.2 trails GPT-5.5 by only 4 points on the AA Intelligence Index and Claude Fable 5 by 9 points—Fable 5 was restricted by the US government three days post-launch over a single jailbreak. The post argues that scaling parameters and data makes models worse at saying “I don’t know,” and frames an unsolved trilemma: raw capability, hallucination calibration, and compute efficiency. The article does not disclose GLM-5.2’s training data size or exact release date.

Zhipu's GLM-5.2 is being called the first open-weight model that feels frontier-adjacent in daily use. Jeremy Howard rated it at least as good as Opus 4.8 and GPT-5.5, though it lacks vision. Artificial Analysis placed it above GPT-5.5 on a new knowledge-work eval. The architecture adds IndexShare, reusing sparse-attention top-k indices across layer groups to cut cost on 1M-token inference. Z.ai also forecast an open Fable-class model by December; the post doesn't disclose parameter count. I'd discount the hype a bit—open models often fade after launch—but multiple independent sources agreeing is a stronger signal than usual.

Z.ai's GLM-5.2 scores 51 on the Artificial Analysis Intelligence Index v4.1, ahead of MiniMax-M3 (44) and DeepSeek V4 Pro (44), making it the top open-weights model. It keeps the same 744B-total / 40B-active parameter count as GLM-5.1 but posts big gains in scientific reasoning and agentic tasks—HLE jumps 12 points to 40%, CritPt up 16 points to 21%. On GDPval-AA v2, a real-world agent benchmark, it hits 1524, effectively level with GPT-5.5 (xhigh reasoning). The trade-off: it averages 43k output tokens per task, up from 26k on GLM-5.1. API pricing stays at $1.4/$4.4/$0.26 per 1M input/output/cache-hit tokens, context window expands from 200K to 1M, and it ships under an MIT license.

Z.AI open-sourced GLM-5.2, a model built for long-horizon coding tasks. It delivers a genuinely usable 1M-token context—not just accepting more tokens, but maintaining quality across long agent trajectories. IndexShare reuses one indexer across every four sparse attention layers, cutting per-token FLOPs by 2.9× at 1M context; MTP acceptance length improved by up to 20%. On FrontierSWE it beats GPT-5.5 by 1%, and on PostTrainBench it outranks both GPT-5.5 and Opus 4.7, placing second. It's the top open-source model across all three long-horizon coding benchmarks. MIT license, no regional restrictions.

Z.ai released GLM-5.2 over the weekend under an MIT license. The 744B MoE model targets coding and long-horizon agent tasks. Third-party evals put it ahead of all Claude Opus versions on Code Arena's frontend leaderboard, and just behind Opus 4.8 overall. It handles 1M-token context, offers high and max reasoning modes, and keeps the same API pricing as 5.1 at $1.4/$4.4 per million input/output tokens. Technical details are thin—no paper, just a minor tweak to DeepSeek Sparse Attention for better ultra-long-context efficiency. Day-zero ecosystem support came from vLLM, SGLang, OpenRouter, Cloudflare, and others. Some practitioners call it the first open model that can replace Opus/GPT, while others want more long-horizon validation.

Zhipu's GLM-5.2 targets coding and long-horizon agent tasks with a 1M-token context window, available now to GLM Coding Plan subscribers. API access and MIT-licensed open weights are promised next week. The post doesn't disclose benchmark scores or parameter count. I'd hold off until weights actually land and third-party evals appear.

Zhipu's new flagship GLM-5.2 is live for Coding Plan subscribers, emphasizing coding strength and 1M context. API and chatbot access arrive next week, alongside an MIT-licensed open-source release. The post doesn't disclose benchmark scores or pricing details.

Zhipu released GLM-5.2, its strongest open-source model yet, available tonight to all GLM Coding Plan users. It supports a genuinely usable 1M context window, leads in long-range tasks, and is called the strongest domestic coding model by Zhipu. API access arrives next week, and the model goes open-source under MIT license next week.

Microsoft AI says MAI-Thinking-1 uses three mechanisms—thermostat, circuit breaker, and self-distillation—to keep RL training stable for several thousand steps; the RSS snippet contrasts MAI’s discipline with DeepSeek’s efficiency and GLM’s endurance.

Zhipu plans to apply for an A-share issuance and STAR Market listing, with new shares accounting for 2% to 8% of post-issuance equity and proceeds allocated to foundation models, a model MaaS platform, and working capital.

The author runs Qwen3.6 27B on a $6,406.45 local server with 4 MI100 GPUs, processing 20.4M input tokens and 1.32M output tokens per day; using OpenRouter prices, the first-year local cost is $2,992.72 versus $3,701.10 for API use.

Zhipu released the GLM-5.1-highspeed API to selected enterprise customers on May 22, with a claimed output speed of 400 tokens/s, built by the GLM team and TileRT team through system-level optimization.

Zhipu GLM-5.1 high-speed API claims 400 tokens/s, and the post says TileRT reconstructs GPU inference at the execution-model level; the RSS snippet does not disclose benchmark conditions, hardware, pricing, or latency distribution.

Zhipu deployed ZCube in a thousand-GPU GLM-5.1 production inference cluster, replacing ROFT while keeping GPUs, software stack, and business code unchanged; throughput rose by over 15%, TTFT P99 fell 40.6%, and switch plus optical module costs dropped by one third.

Interfaze released Structured Output Benchmark, scoring schema pass rate, types, and value accuracy across text, image, and audio. Each record has a JSON Schema and human plus LLM-checked ground truth; GLM-4.7 ranks No. 2 overall. The key bug is field-level value error: GPT-5.4 ranks 3rd on text and 9th on images.

Ruan Yifeng’s Issue 381 excerpts talks from Beijing’s AGI-Next summit on Jan 10, covering views from Zhipu, Alibaba Qwen, and Tencent AI leaders on China’s model roadmap. The post cites Lin Junyang saying US compute is 1-2 orders of magnitude larger, Yao Shunyu calling the odds of a China-led top AI company in 3-5 years high, while Lin puts it at 20%. The key split is strategic: Tang Jie points to RLVR in 2025, Lin bets on multimodal foundation agents, and Yao says B2B buyers pay a $200/month premium for stronger models.