Benchmark announcement from **Artificial Analysis** (independent AI model evaluation platform, via X/Twitter + model page): **GLM-5.2** by **Z.ai** (Zhipu AI, @Zai_org) becomes **the leading open weights model** and climbs to **#3 in the overall ranking** of **GDPval-AA**, a real-world benchmark for *economically valuable knowledge work* (long-horizon, multi-turn, agentic tasks). GLM-5.2 scores **1524 Elo**, behind only **Claude Fable 5 (1783)** and **Claude Opus 4.8 (1615)**, and on par with **GPT-5.5 (xhigh, 1509)**. It leads the next open model (**MiniMax-M3, 1408**) by a wide margin, as well as numerous proprietary models: **Gemini 3.5 Flash (1357)**, **Qwen 3.7 Max (1289)**, **Muse Spark (1158)**. The tasks are genuinely agentic: **~31 turns per task** on average across **1,999 matches**. The same hierarchy holds on the **Artificial Analysis Intelligence Index** (1st among open weights), the **Agentic Index** (#3), and **AA-Briefcase** (#3, ahead of GPT-5.5 xhigh, behind Fable 5). Key highlight: an **open weights** model under **MIT license**, **MoE with 753B parameters / 40B active**, **1M token** context, priced at **$1.40/$4.40 per 1M tokens** input/output, rivals the proprietary frontier on agentic work — a real step for open models.
Editorial by Andrew Ng in The Batch #350 laying out an **acceleration hierarchy driven by coding agents** by type of software work: **Frontend (max) > Backend (moderate) > Infrastructure (low) > Research (minimal)**. The rationale rests on implicit *verifiability* (fluency in TypeScript/JavaScript + an autonomous agent–browser loop on the frontend) and on the LLMs' blind spots (corner cases / security / DB migrations for the backend, opaque network tradeoffs for infra, irreducible hypothesis formation for research). The issue also covers 4 structuring news items: **GLM-5.1 (Z.ai)**, a 754B/40B-active-parameter MIT-licensed model capable of 8-hour autonomous tasks (SWE-Bench Pro leader at 58.4%); **Digit (Agility Robotics) at Schaeffler**, the first industrial deployment of humanoids (5'9"/143lb, $10–25/h vs $20/h for a human); the **anti-data-center revolt** (~$64B in projects blocked May-2024 / March-2025, Maine moratorium on 20MW+, a molotov cocktail at Sam Altman's home); and the **"assistant axis"** (Christina Lu, MATS / Oxford / Anthropic), which reduces persona drift and jailbreaks (Qwen3 32B: 83%→41%; Llama 3.3 70B: 65%→33%) without degrading IFEval/GSM8k/MMLU-Pro/EQ-Bench.
#Andrew Ng#The Batch#DeepLearning.AI
Andrew Ng (édito principal — fondateur DeepLearning.AI, Stanford, ex-Google Brain, ex-Baidu) ; rédaction The Batch (DeepLearning.AI) pour les sections actualités