A collection of 14 posts
GLM-5.3-Flash is 320B-A18B, multimodal and roughly 9x cheaper than GLM-5.2's 744B-A40B. A verified head-to-head on specs, benchmarks, cost and self-hosting — plus the cases where GLM-5.2 still wins.
GLM-5.3-Flash ships 320B parameters in a 328 GB native-FP8 checkpoint, so the 18B active count tells you nothing about the memory you need. Here are the real hardware requirements by quantisation, plus working vLLM, SGLang, llama.cpp and Apple Silicon setups.
Z.ai's GLM-5.3-Flash is the model that ran anonymously as Ox Alpha: a 320B-total, 18B-active natively multimodal MoE with a 1M-token context and MIT open weights. Here are the verified specs, pricing and benchmarks.
GLM-5.3 claims 2,436 real vulnerabilities found — and FreeBSD and Red Hat CVEs actually credit the model line. What verifies, what doesn't, and the licence question that matters most.
GLM-5.3 launched August 14, 2026 with big coding claims and cyber capabilities. The API and independent benchmarks have since arrived — the weights haven't. What shipped, what's verified, and whether to wait.
Two new MIT open-weights coding models shipped a day apart in June 2026. We compare architecture, coding benchmarks, local hardware, and API pricing for Ornith 1.0 vs GLM 5.2 — with an honest, no-hype verdict on which to pick.
A coding head-to-head: GLM-5.2's leaderboard-topping text coding vs MiniMax M3's native multimodality, lower price, and MSA long-context speed — with specs, benchmarks, and a clear verdict.
A practical walkthrough for self-hosting GLM-5.2 (744B MoE, 40B active) on llama.cpp. Quant tables, four hardware paths, exact install commands, verification, and a fallback to the Z.ai cloud API if your rig falls short.
Z.ai's GLM-5.2: 744B params (40B active), 1M-token context, MIT-licensed weights — still the newest GLM you can self-host while GLM-5.3's weights are pending. Architecture, benchmarks, pricing, and a 3-path local-inference playbook.
Moonshot's Kimi K2.7 Code and Z.ai's freshly-released GLM 5.2 are both Chinese open-weights coding flagships, both shipped in June 2026, and they trade on opposite axes. K2.7 leads on MCP tool use and pricing; GLM 5.2 leads on 1M context. We pick per workload.
GLM 5.2 ships 1M-token context and MIT open weights on a flat subscription. Claude Opus 4.8 stays the agentic-coding benchmark at premium per-token pricing. We compare cost, agentic strength, self-hosting and pick a winner per workload.
Two open-weights heavyweights from China go head-to-head for the agentic-coding throne. GLM 5.2 leads on context window; DeepSeek V4 leads on token economics. We break down cost, agentic strength, self-host paths, and pick a winner per workload.
OpenAI's flagship versus Z.ai's freshest open-weights challenger. GPT-5.5 holds frontier coding benchmarks; GLM 5.2 ships a 1M window and self-hostable weights. Where each actually wins for engineering teams.
Zhipu Z.ai shipped GLM 5.2 today on every GLM Coding Plan tier with a usable 1M-token context window. Standalone API, the Z.ai chatbot, and the MIT open weights are arriving next week. No benchmarks yet — here's what's confirmed, what's not, and how it fits next to GLM-5.1.