Gemma4 26B Q8 vs Qwen3.5 27B Coding Benchmarks
💡Dense models hit 100% fixes in coding evals—key for local agent builders
⚡ 30-Second TL;DR
What Changed
Qwen3.5-27B Q4 and Gemma4-31B Q4 achieve 37/37 fixes (100%) with 0 regressions
Why It Matters
Dense models like Qwen3.5-27B excel in local coding agents, offering perfect fixes with better efficiency than MoE. Quantization boosts Gemma4-26B but not to top tier. Highlights trade-offs for local deployment.
What To Do Next
Benchmark Qwen3.5-27B Q4_K_XL on your local coding eval suite using llama.cpp.
Key Points
- •Qwen3.5-27B Q4 and Gemma4-31B Q4 achieve 37/37 fixes (100%) with 0 regressions
- •Gemma4-26B Q8 improves to 17 fixes (45.9%) from Q4's 28
- •Qwen3.5-27B most token-efficient at ~16K tokens per fix
- •Tool calls highest for Qwen3.5-27B (181 total, 99.4% success)
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Original source: Reddit r/LocalLLaMA ↗
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