发生了什么: GLM-4.7-Flash-Coder

GLM-4.7-Flash-Coder, a 30B mixture-of-experts model, improved agentic coding performance from 33.15% to 41.65% on SWE-rebench-V2 with 14% lower inference cost.

为何重要: GLM-4.7-Flash-Coder

Engineers building code-generation agents or evaluating smaller models against larger competitors should assess this model's cost-performance tradeoff for their specific workloads.

注意: GLM-4.7-Flash-Coder

The model was trained on successful trajectories from a teacher model using rejection sampling; performance on novel or out-of-distribution tasks remains unclear.

新闻

GLM-4.7-Flash-Coder

White Circle · 发布于 2026年9月21日 · 阅读约3分钟

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发生了什么: GLM-4.7-Flash-Coder, a 30B mixture-of-experts model, improved agentic coding performance from 33.15% to 41.65% on SWE-rebench-V2 with 14% lower inference cost.
为何重要: Engineers building code-generation agents or evaluating smaller models against larger competitors should assess this model's cost-performance tradeoff for their specific workloads.
注意: The model was trained on successful trajectories from a teacher model using rejection sampling; performance on novel or out-of-distribution tasks remains unclear.

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Agentic Context Engineering (Evolving Playbook)
SWE-bench Suite
Cost-Aware Model Selection

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