A collection of 15 posts
Z.ai added GLM-5.3 Prime and GLM-5.3 FlashX in September 2026. Neither is a new model — both are accelerated serving tiers of weights that already shipped. Here is the full GLM-5.3-era lineup, with verified prices, licences and a decision rule.
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 credit the model line. What verifies, what doesn't, and what the licence turned out to say when the weights shipped on 25 August 2026.
GLM-5.3 launched August 14, 2026 with big coding claims and cyber capabilities. Both variants now have open weights — GLM-5.3-Flash shipped under plain MIT, GLM-5.3 under a custom licence. What shipped, what's independently verified, and which variant to run.
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.
GLM-5.2 leads MiniMax M3 by 4.5 points on Artificial Analysis's Intelligence Index v4.3.2 — but only at its Max reasoning tier. M3 wins on price and owns image and video input. GLM-5.3-Flash now beats both on capability and cost.
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.
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.
Claude Opus 4.8 leads GLM 5.2 on Artificial Analysis's Intelligence Index v4.3.2 (41.79 vs 33.71), and both carry 1M context. GLM 5.2's real edge is MIT weights — and GLM-5.3-Flash now matches Opus 4.8 at a sixteenth of the cost per task.
GPT-5.5 leads GLM 5.2 on Artificial Analysis's Intelligence Index v4.3.2 (38.36 vs 33.71), and both carry roughly 1M context. The per-task cost gap is 1.8x, not the 6.8x the per-token rates imply.
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.