9 results for “GLM-5.2”
Z.ai launched GLM-5.3 on 14 August 2026 on the same base as GLM-5.2, claiming large coding and cyber gains from post-training alone. Open weights are due in about two weeks; Coding Plan users already get the new model.
DeepSeek, Qwen, and GLM-class APIs often price an order of magnitude below Claude Opus, GPT-5.6 Sol, and peers. The useful question is which tier matches the job you intend — and how close the outcome gets to that intention without paying for unused ceiling.
Z.ai released GLM-5.2 on June 13 with 1M context, MIT weights, and Coding Plan access before benchmarks. Developers can evaluate it in agents today while GLM-5.5 remains entirely unconfirmed.
A JPMorgan note relayed by Reuters and CGTN forecasts a Zhipu AI model exceeding 1 trillion parameters for August 2026. Zhipu has not confirmed the model, its specs, or the date — leaving developers to separate analyst signals from vendor commitments.
Zhipu AI compressed its model releases to two-month intervals across early 2026, culminating in GLM-5.2's June launch with a 1-million-token context window. The cadence signals a production-line strategy that holds parameter count steady while iterating context, tooling, and pricing to undercut U.S. rivals on agentic coding workloads.
Z.ai released GLM-5.2, a 753B open-weight model with MIT licensing that matches Anthropic's Opus 4.8 on agentic benchmarks at roughly one-fifth the token cost. The 1M-context model is already running in Cursor and Claude Code, shifting enterprise leverage toward self-hosted inference.
AMD, Super Micro Computer and Spectro Cloud have jointly unveiled AMD Instinct Coder, a turnkey enterprise AI coding platform designed to give. Enterprise AI coding has moved from individual developer experimentation to a platform-level.
Chinese AI startup DeepSeek has released its V4-Flash model at a price point that dramatically undercuts every major competitor, according to benchmark. The emergence of V4-Flash accelerates a structural shift in the AI market.
A joint evaluation by the UK Artificial Intelligence Safety Institute (UK AISI) and the US Center for AI Standards and Innovation (CAISI) has revealed. The evaluation underscores a growing concern among safety institutions: the steady.