GLM-5.2 Released with 1M Token Context and 753B Parameters Under MIT License

Zhipu AI has released GLM-5.2, a 753 billion parameter model that delivers a 1 million token context window under an MIT open-source license. The model represents an architectural shift with its IndexShare feature, which reduces per-token FLOPs by 2.9× at 1M context length by reusing the same indexer across every four sparse attention layers.

Benchmark Performance

According to Zhipu AI, GLM-5.2 achieves:

• SWE-bench Pro: 62.1% (versus 58.4% for GLM-5.1)
• GPQA-Diamond: 91.2% (versus 86.2% for GLM-5.1)
• NL2Repo: 48.9% (versus 42.7% for GLM-5.1)
• DeepSWE: 46.2% (versus 18% for GLM-5.1)
• AIME 2026: 99.2%
• HLE reasoning: 40.5% (54.7% with tools)

The company positions GLM-5.2 competitively against Claude Opus 4.8, GPT-5.5, and Gemini 3.1 Pro on long-horizon task benchmarks, though these comparisons represent company claims rather than independent verification.

Technical Architecture

GLM-5.2 introduces two key architectural improvements:

1. IndexShare: Reuses indexers across sparse attention layers, reducing computational requirements at extended context lengths
2. Enhanced MTP layer: Improved for speculative decoding, increasing acceptance length by up to 20%

The model supports multiple "thinking effort levels" for coding tasks, allowing developers to balance performance against latency requirements.

Pricing and Availability

Pricing not yet disclosed. The model is available through:

• Z.ai API Platform for hosted inference
• Local deployment via SGLang (v0.5.13.post1+), vLLM (v0.23.0+), xLLM (v0.10.0+), Transformers (v0.5.12+), and KTransformers (v0.5.12+)
• Hugging Face model hub (zai-org/GLM-5.2)

What This Means

GLM-5.2's MIT license removes geographic restrictions common in other frontier models, potentially accelerating adoption in regions where licensing has been restrictive. The architectural focus on computational efficiency at extreme context lengths—2.9× reduction in FLOPs—addresses a critical bottleneck as context windows expand industry-wide. However, the benchmark scores show GLM-5.2 trailing Claude Opus 4.8 and GPT-5.5 on most coding benchmarks, with particular gaps on SWE-bench Pro (69.2% vs 62.1%) and NL2Repo (69.7% vs 48.9%), suggesting it competes more directly with open-weight alternatives than proprietary frontier models.