Qwen3.6-27B Looks Viable for Local Agent Planning, Not Ungated Execution

A detailed r/LocalLLaMA report tested Qwen3.6-27B as the reasoning layer inside a multi-agent coding orchestrator for two weeks. The setup used a single RTX 3090 with 24GB of VRAM, Qwen3.6-27B at Q6_K through Ollama, and an orchestrator with lead, manager, and sub-agent loops. The author says the model was evaluated across 47 multi-step coding workflows over two real repositories.

The strongest results were in planning and review. Qwen3.6 produced coherent multi-step plans and reached roughly 95% schema-valid output after prompt tuning, according to the post. Memory extraction also worked well enough for a Mem0-style loop, and a second Qwen instance used for review caught about 60% of the bugs that Claude caught on the same output. For a local model running on consumer hardware, that is a meaningful result.

The weaker boundary was tool execution. The author reports that Qwen3.6’s JSON tool-call output had about a 12% format error rate across the 47 tasks, versus roughly 0.5% for Claude on the same workload. These were not only malformed JSON errors. The post describes wrong field names, wrong types, and hallucinated tool signatures. Strict-output tools reduced the problem but did not remove it.

Long context created another practical limit. Past about 14k accumulated tokens, the model reportedly began misremembering earlier decisions, with a working limit closer to 12k before summarizing and resetting. Failure recovery was also uneven: when a sub-agent failed, Qwen sometimes continued as if it had succeeded, producing three cascading hallucination cases in the test set.

The report’s value is that it draws a useful line. Qwen3.6-27B may be good enough as a local planning and reasoning layer, especially where privacy or cost matters. It should not be treated as a drop-in execution layer without gates. Plan approval, structured-output enforcement, and external failure detection are still part of the system design.

That is a more practical takeaway than a simple cloud-versus-local debate. Local agents are becoming plausible, but the trust boundary belongs in the architecture, not in the model prompt.

Source: Reddit r/LocalLLaMA.