GLM-5.2

GLM-5.2 is Zhipu AI's flagship model for long-horizon tasks, released June 13, 2026. Built on the 744B-parameter Mixture-of-Experts architecture from the GLM-5 line, it delivers a solid 1M-token context, stronger coding with flexible thinking effort, and long-horizon agentic performance. Weights are open under the MIT license, and the model ranks second on Arena's Code Arena Frontend board, ahead of Claude Opus 4.7 and 4.8 in thinking mode.

Core Features

• Solid 1M context: A 1M-token window that stably sustains long-horizon work, enabled via the glm-5.2[1m] model id with up to 131,072 output tokens.
• Advanced coding with flexible effort: Stronger coding with High and Max thinking-effort levels to balance performance and latency.
• IndexShare architecture: Reuses the same indexer across every four sparse attention layers, cutting per-token FLOPs by 2.9x at 1M context; improved MTP layer raises speculative-decoding acceptance by up to 20%.
• Pure open: MIT open-source license, weights on Hugging Face and ModelScope, supporting transformers, vLLM, SGLang, xLLM, and ktransformers.
• Agent-native: First-day support for Claude Code, Cline, OpenCode, Roo Code, Goose, OpenClaw, and Kilo Code via an OpenAI-shaped chat completions API.

Model Specifications

Pricing

GLM Coding Plan subscriptions (Lite/Pro/Max/Team) start around $18/month; GLM-5.2 consumes quota at 3x during peak hours (14:00-18:00 UTC+8) and 2x off-peak.

Benchmark Highlights

• Arena Code Arena Frontend: #2, ahead of Claude Opus 4.7/4.8 in thinking mode.
• SWE-bench Pro: Outperforms GPT-5.5, trailing just behind Claude Opus 4.8.
• Cost: Z.ai claims GLM-5.2 beats GPT-5.5 on long-horizon coding at about 1/6 the cost.

Use Cases

• Long-horizon agentic coding: Multi-step tasks where the model must stay coherent over a long session with full-codebase context.
• Repo-scale work: Feed an entire front-end, back-end, database logic, and docs into a single 1M-context call.
• Cost-sensitive agents: Frontier-adjacent coding performance at a fraction of closed-lab pricing.
• Self-hosted deployments: MIT weights run on your own GPU with vLLM, SGLang, or ktransformers.

Advantages

1. Open with 1M context: MIT-licensed weights plus a genuinely usable million-token window.
2. Frontier-adjacent coding: Second on the front-end coding leaderboard behind only the banned Claude 3.5.
3. Cost leader: ~$1.40 in / $4.40 out per 1M tokens versus $30/$180 for GPT-5.5.

Tips

1. Use the [1m] suffix to enable the full context window in coding agents like Claude Code.
2. Pick thinking effort by task: Max for hard refactors, High for routine work to save latency and quota.
3. Cache long prefixes: The ~$0.26 cached-input rate cuts repeated system-prompt costs by ~81%.

Conclusion

GLM-5.2 makes 1M-context, long-horizon agentic coding affordable and open. If you want frontier-adjacent coding performance without closed-lab pricing or regional limits, it is the strongest MIT-licensed option in its class today.