Re:Qwen3.5 122B and 35B models offer Sonnet 4.5 performance ...
by mmis1000 on Sunday March 01, 2026 @01:22AM
(#47204193)
from my personal experience, qwen 30b a3b understand command quiet well as long as the input is not big enough that ruin the attention (I feel the boundary is somewhere between 8000 or 12000?). But that isn't really bug of model itself though. A smaller model just have shorter memory, it's simply physical restriction.
I made a mixed extraction, cleaning, translation, formatting task on job that have average 6000 token input. And so far, only 30b a3b is smart enough not miss job detail (most of time)
I later refactor the task to multi pass using smaller model though. Make job simpler is still a better strategy to get clean output if you can change the pipeline.
I run a multi-model setup for production work - routing different tasks to different models based on what actually works, not benchmark scores. What I've found over the past 6 months:
- Structured output (JSON extraction, classification, reformatting): open models at 30B+ are genuinely competitive. Qwen and Gemma both handle this reliably.
- Code completion and inline suggestions: small MoE models are surprisingly good here because the task is inherently constrained by surrounding context.
- Complex multi-step reasoning, long-horizon planning, or tasks requiring deep domain knowledge: frontier closed models still win by a significant margin.
The practical takeaway isn't "open models match Sonnet" or "benchmarks are lies" - it's that the cost-performance frontier has shifted enough that a thoughtful routing strategy (cheap/local model for 70% of tasks, frontier API for the 30% that actually needs it) is now viable in a way it wasn't even 6 months ago. That's the real story here, not whether Qwen exactly matches Sonnet on some leaderboard.