jietang
15 Intel
Jie Tang is a Chair Professor of Computer Science at Tsinghua University and a co-founder of Z.ai (Zhipu AI), where he has led development of the GLM family of large language models. A Fellow of the ACM, AAAI, and IEEE, he built the academic search engine AMiner and has published more than 400 papers on data mining, social networks, and artificial general intelligence.
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Intel
Zhipu says GLM-5.3 Flash scored 57 on the Artificial Analysis index at roughly 1/100th of frontier model pricing while running on domestically made chips, and became OpenRouter's top model by weekly token share.
GLM-5.3 is coming. focusing on coding. https://t.co/g10V8eJIwq
Zhipu researchers describe Single-rollout Asynchronous Optimization (SAO), a reinforcement-learning method that stays stable for a thousand training steps and outperforms GRPO and its variants on SWE-Bench Verified, BeyondAIME, and IMOAnswerBench.
Zhipu's Jie Tang argues long-horizon agentic tasks are the next breakthrough. He says memory and continual learning are being approximated by long context and faster releases, self-judging is the tipping point, and self-training is the endgame. Several claims are speculative.
GLM-5.2 debuts at the top of open-source rankings on the Artificial Analysis Intelligence Index (score 51, matching Claude Opus 4.8), ranks #2 on Code Arena, #1 on DesignArena, and #3 globally on the FrontierSWE engineering benchmark.
Zhipu is rolling out GLM-5.1-highspeed, a pricier variant running at 400 tokens per second aimed at agentic workloads that need much faster generation than standard inference.
Inference providers are racing to serve GLM-5.2 faster, with reported throughput climbing from 280 to 318 tokens per second and Databricks later claiming 392, showing how much headroom remains in open-weight model serving.
Zhipu releases GLM-5.2 under an MIT license with a 1M-token context trained for coding-agent work. IndexShare attention cuts per-token FLOPs 2.9x at 1M, and it ranks highest among open models on FrontierSWE, PostTrainBench and SWE-Marathon.
1M users already https://t.co/lLKMytzbSk
Artificial Analysis Index = 60 https://t.co/ts3QfDtRmv
Zhipu released GLM-5.2 as a fully open-weight model with a usable 1M-token context window, positioning it as its strongest coding model and framing the release as a response to sudden restrictions on frontier model access elsewhere.
A history of compute-optimal scaling from Kaplan's parameter-heavy exponents through Chinchilla's 20-tokens-per-parameter rule to today's deliberate over-training, driven by inference cost now dominating a model's lifetime spend rather than training cost alone.
ZCode is offering GLM-5.3-Flash free for 10 hours a day from September 3 to 18, prioritizing Coding Plan subscribers, with new users receiving a one-time 100M free token allotment during the window.
Zhipu's Jie Tang describes how a GLM-5.3-powered Infra Agent took GLM-5.3-Flash from first run on new accelerators to full production serving in two weeks, tripling throughput by giving the agent layered 'dense feedback' instead of only slow, sparse end-to-end benchmarks.
ZCode 3.0 launches optimized for GLM-5.2, adding stronger agentic task execution, better long-context coding, and a new Goal feature for managing multi-step development objectives end to end.