在基于HTML的波浪绘制任务中，Qwen3.6 27B模型虽生成速度较慢（24 tok/s），耗时更长，但其输出结果比速度更快的35B模型更清晰、一致。该任务强调布局、顺序和内部规划能力，表明较长的推理时间可以弥补延迟劣势。这揭示了任务性质对模型选择的关键影响：27B更适合需要稳定执行结构化计划的任务，而35B则在追求快速响应的日常场景中占优。研究说明，处理结构化模式的任务主要奖励步骤排序和内部一致性，而非单纯的语言流畅度。
Very interesting findings by @atomic_chat_hq :
Qwen3.6 27B beat 35B on a planning-heavy HTML-based wave drawing task despite being little slower.
On the same MacBook Pro M5 Max 64GB, with Google TurboQuant
- 35B finished in 2m10s, with 65tok/s
- 27B ran at 24tok/s and finished in 5m22s.
HTML wave drawing stresses layout, sequencing, and internal planning, so longer reasoning can beat lower latency.
Results from 35B was fast but messy, while 27B produced cleaner and more consistent waves.
Tasks like drawing structured patterns are not mainly about raw verbal fluency. They reward step ordering, internal consistency, and the ability to hold a simple plan steady from start to finish.
A model optimized for quick everyday replies can look strong on speed while still drifting on tasks where each line constrains the next.
Atomic Chat can run local models like Llama, Gemma, and Qwen, but can also connect to cloud providers when needed, which is a more realistic design than pretending every task should be fully offline.
Compared Qwen3.6 35B and 27B in the same conditions with Google TurboQuant Device: MacBook Pro M5Max 64GB RAM Outputs characteristics: Qwen3.6 35B: 6672 tokens, 2m 10s, 65 tok/s Qwen3.6 27B: 7344 tokens, 5m 22s, 24 tok/s Conclusion: Both models were asked to draw waves using HTML, 35B responded quickly but the result feels weak and messy, while 27B took more time and delivered a much cleaner and more consistent result, because it is built for thinking and planning, so it works better on tasks that need structure, overall 27B is a better choice for tasks where planning matters, while 35B is more suitable for everyday use when you just need a fast response在 X 查看被引用的帖子
来源：Rohan Paul · x.com