Z.ai drops GLM 5.2 with 1M context as Fable 5 goes offline
Z.ai shipped GLM 5.2 on June 13 with a 1-million-token context window and zero benchmarks. The same day, Anthropic’s Fable 5 and Mythos 5 went offline under a US export control order. The timing was not an accident.
Developers who woke up to broken Fable API calls suddenly had a drop-in alternative. GLM 5.2 exposes an Anthropic-compatible endpoint, which means Claude Code users need three config changes to switch: set both ANTHROPIC_DEFAULT_SONNET_MODEL and ANTHROPIC_DEFAULT_OPUS_MODEL to glm-5.2[1m], set the auto-compact window to a million, and that is it. Day-one support extends to Cline, OpenCode, Roo Code, Goose, Crush, OpenClaw, and Kilo Code. Eight coding agents, zero migration overhead.
The headline number is real. A 1M-token context window holds an entire mid-sized codebase in working memory: source files, tests, config, conversation history. Maximum output is 131,072 tokens per response, roughly five times GLM-5.1’s limit. GLM-5.1, the predecessor, ran eight-hour autonomous coding sessions with up to 1,700 agent steps. The expanded context eliminates the summarization cycles that break long agentic tasks.
For comparison, most competing models cap at 128K to 200K context. Going from 200K to 1M is the difference between a model that keeps state through a large feature and one that forgets it halfway through a refactor.
But here is what GLM 5.2 does not ship with: benchmarks. No SWE-bench Verified, no LiveCodeBench, no HumanEval. Z.ai says the model is “superior to prior GLM versions on long-horizon coding” without a single number to back it up. The standalone API and Z.ai chatbot are not available yet. The only way to use the model today is through their GLM Coding Plan subscription.
One X reviewer put it bluntly: “A flagship model launch with zero performance data and a paywalled-only entry point is not how you compete with Opus 4.8 or GPT 5.5. It’s how you hide a weak model behind a subscription wall.”
The GLM track record matters here. GLM-5, released in February, scored 77.8% on SWE-bench Verified. GLM-5.1 hit 58.4% on SWE-bench Pro, slightly edging Claude Opus 4.6’s 57.3%, and reached 1,530 Elo on Code Arena. A Hacker News commenter calibrates it as “about six months behind the frontier labs. Very similar to Opus in January. Pretty damn impressive and very useable.”
An independent Pac-Man coding test on r/LocalLLaMA ranked GLM 5.2 in first place ahead of Qwen 3.6 27B. The one-shot output was nearly functional, with only a minor ghost AI bug that a single follow-up prompt fixed.
The open-weight coding model space is crowded. Moonshot’s Kimi K2.7-Code dropped the day before. MiniMax M2.5, Qwen3-Coder, and DeepSeek V3.2 are all pushing SWE-bench scores into the high 70s and low 80s. GLM 5.2’s 1M context is the differentiator most of those competitors lack right now.
MIT-licensed weights, the standalone API, and a technical report arrive the week of June 16. The license shift from GLM-5’s Apache-2.0 to MIT is notable for a model at this capability tier. For teams rethinking their reliance on a single commercial provider after this week’s export control chaos, local deployment is the real pitch for GLM 5.2.