Evaluations are finished for GLM 4.7.

• It is neck and neck with DeepSeek V3.2 (Thinking) on SWE-bench Verified, 67.0% and 68.8% respectively. This is a +11% bump on SWE-bench Verified compared to GLM 4.6
• On Terminal-Bench, it was one of the top open-weight models, along with DeepSeek V3.2 (Nonthinking)
• Overall, it is more token efficient than its predecessor, and was much cheaper per-task, despite having the same input and output pricing.

The model was tested with temperature=1 and default top_p for all benchmarks but SWE-bench Verified and Terminal-Bench, which used temperature=0.7 and top_p=1. Reasoning was enabled for all benchmarks.

GLM 4.7 debuts at #1 on our open-weight leaderboard.

• It is #9 on the overall leaderboard, behind models from the likes of OpenAI, Gemini, and Anthropic. It is currently the only open-weight model in the top ten.
• It is a significant performance improvement over GLM 4.6, with a +9.5% performance increase.
• Despite being priced the same per-token, we also found that it was cheaper than GLM 4.6, using tokens more efficiently.
• The model uses a new interleaved thinking mode, which may explain the large bump in performance.

The model was tested with temperature=1 and default top_p for all benchmarks but coding benchmarks, which used temperature=0.7 and top_p=1. Reasoning was enabled for all benchmarks.

Results on full benchmarks will be released soon!