Why GLM 5.2 stands out

GLM 5.2 scores 80 on the BenchLM composite leaderboard, tying DeepSeek V4 Pro as the top open-weight model. It has an MIT license (most permissive for commercial use), a 1M token context window, and strong performance across coding, reasoning, and agentic benchmarks.

At 744B total parameters with 40B active per token (MoE architecture), it balances capability with inference efficiency.

Access methods

API: Available via Z.AI's API at $0.98/M input tokens.

Self-host: Download weights from Hugging Face and serve with vLLM or SGLang.

Cloud: Available on OpenRouter with competitive pricing.

Best use cases

Production RAG pipelines that need long-context retrieval (1M window).

Agentic workflows requiring tool use, planning, and multi-step reasoning.

Commercial applications where the MIT license eliminates legal uncertainty.

Multi-language applications with strong Chinese and English support.

Hardware requirements for self-hosting

The 40B active parameters per token mean inference requires approximately 80GB of GPU memory (two A100s or one H100).

For full 744B MoE: multi-node setup with 8+ GPUs recommended.

Quantized versions (4-bit) can run on a single A100 80GB.

FAQ

Is GLM 5.2 truly open source?

The weights are open-weight under MIT license. Training data is not released, so it does not meet the OSI definition of open source, but the MIT license permits unrestricted commercial use.

How does GLM 5.2 compare to DeepSeek V4?

Both score 80 on the leaderboard. GLM 5.2 has an MIT license (more permissive than DeepSeek's), while DeepSeek V4 has slightly better pricing at $0.435/M tokens.

Use GLM 5.2

  1. Choose access: API via Z.AI, self-host via vLLM, or OpenRouter
  2. For self-hosting: download weights from Hugging Face
  3. Serve with: vLLM, SGLang, or TGI
  4. Configure for your use case: RAG, agentic, or chat
  5. Leverage 1M context for document-heavy workloads
  6. Review MIT license terms for commercial deployment

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