GLM-5.2: Built for Long-Horizon Tasks
ai on Hugging Face Back to Articles a]:hidden"> GLM-5. 2: Built for Long-Horizon Tasks Team Article Published June 17, 2026 Upvote 38 +32 Z. AI zaiorg Follow zai-org We're introducing GLM-5.
Key Takeaways
- 2, our latest flagship model for long-horizon tasks.
It marks a substantial leap in long-horizon task capability over its predecessor GLM-5.
- This capability is reflected in GLM-5.
- 8 by 13% while remaining second only to the Opus series.
- 0) - while staying ahead of Gemini 3.
2 also introduces effort level control, enabling users to explicitly balance model capability against task execution speed and computational cost.
- Architecture for 1M Context IndexShare for DSA To support 1M context length, in GLM-5.
Stats & Key Facts
- #2's MTP layer for speculative decoding, increasing the acceptance length by up to 20% Pure Open : An MIT open-source license - no regional limits, technical access without borders Supporting long-horizon tasks starts with making long context engineering-usable: the model must maintain quality across long, messy coding-agent trajectories, not just accept more tokens.
- #8 by 13% while remaining second only to the Opus series.
2, our latest flagship model for long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor GLM-5. 1 and, for the first time, delivers that capability on a solid 1M-token context .
2's MTP layer for speculative decoding, increasing the acceptance length by up to 20% Pure Open : An MIT open-source license - no regional limits, technical access without borders Supporting long-horizon tasks starts with making long context engineering-usable: the model must maintain quality across long, messy coding-agent trajectories, not just accept more tokens. A 1M context is easy to claim, but much harder to keep reliable under real engineering pressure. To this end, we substantially expanded 1M-context training for coding-agent scenarios, covering large-scale implementation, automated research, performance optimization, and complex debugging.
The result is a long-context system that is not only wide in scope, but solid in execution: a practical substrate for sustained engineering work. This capability is reflected in GLM-5. 2's performance on three long-horizon coding benchmarks.
For more details please read the original article at Hugging Face.
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