Qwen 3.6 27B DeepSWE Benchmark Results Highlight Gap Between Local and Closed-Source Models
Original: Qwen 3.6 27B on DeepSWE
Qwen 3.6 27B scored 1.79% on the DeepSWE benchmark, highlighting the persistent performance gap between local open-source and closed-source models.
A community benchmark of Qwen 3.6 27B on DeepSWE yielded a score of 1.79% (18/20th place), slightly outperforming Haiku 4.5. Run on a single RTX 6000 Blackwell GPU via vLLM with reasoning enabled, the test averaged 32 minutes and 44k output tokens per task. The author notes that while Qwen 3.6 27B represents a 'poor man's local SOTA,' the massive gap compared to frontier closed models suggests local LLMs are struggling to keep pace in complex coding.
This is a popular evaluation report from the Reddit r/LocalLLaMA community. The author shared the complete data and insights from running the Qwen 3.6 27B model on DeepSWE (a benchmark that evaluates an LLM's software engineering capabilities).
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