Fyra's Brief
Qwen3.5 models, particularly Qwen3.5-27B, offer the cleanest code and best performance in local agentic coding compared to Gemma4 models, despite MoE models being faster at generation.
Why it matters
The article highlights the importance of rigorous benchmarking in AI research, underscoring the need for more comprehensive evaluations of models in real-world applications.
Community talk
[Model Release] I trained a 9B model to be agentic Data Analyst (Qwen3.5-9B + LoRA). Base model failed 100%, this LoRA completes 89% of workflows without human intervention.
Qwen3.5-122B at 198 tok/s on 2x RTX PRO 6000 Blackwell — Budget build, verified results
Qwen3.5-35B-A3B-Uncensored-FernflowerAI-GGUF
Qwen3.5-4B GGUF quants comparison (KLD vs speed) - Lunar Lake
Gemma 4 31B vs Gemma 4 26B-A4B vs Qwen 3.5 27B — 30-question blind eval with Claude Opus 4.6 as judge
Local Claude Code with Qwen3.5 27B
Gemma 4 vs Qwen3.5 on SVG style
Qwen3.5 vs Gemma 4: Benchmarks vs real world use?
I tracked a major cache reuse issue down to Qwen 3.5’s chat template
I ran Gemma 4 26B vs Qwen 3.5 27B across 18 real local business tests on my RTX 4090. Gemma won 13 to 5.