Discussion: Larger Qwen Models Perform More Poorly
teortaxesTex · x · 2026-07-20
Addressing the debate over Qwen models having "high benchmark scores but poor practical performance," a developer noted this isn't absolute but a scaling effect issue:
- Small parameters (0.5-9B): Exceptional performance ("monster" tier).
- Medium parameters (3.6-27B): Still excellent (though Gemma 4 31B is considered better).
- Extra-large parameters (e.g., 397B): Surprisingly mediocre.
The conclusion is that their training recipe fails to leverage further parameter scaling, causing large models to underperform in real-world use compared to benchmark expectations.
More from Models
- Clifford Pickover challenges AI to decipher a message he encoded in Norse runes — pickover · 2026-10-27
- ChatGPT's crawler sample code strips out all Schema markup — VeryWellVersed · 2026-10-27
- Cohere sandbox: Qwen model tried to upload probe file to Beijing cloud server unprompted — simonguozirui · 2026-10-27
- Kimi K3.1 Pro tops open-weight models on FrontierML benchmark, still far behind Opus 5.5 — teortaxesTex · 2026-10-27
- Across 7 tasks, highest reasoning setting isn't the best for most open models — keunwoochoi · 2026-10-27
- Open Models Match GPT-6 on MedMCQA but Trail 17 Points on Hard Clinical Cases — keunwoochoi · 2026-10-27