🦙Reddit r/LocalLLaMA•Apr 22, 2026Stalecollected in 3h

Gemma4 26B Q8 vs Qwen3.5 27B Coding Benchmarks

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#benchmarks #quantization #moe-vs-dense #tool-callinggemma-4-26b-moe-/-qwen-3.5-27b-/-gemma-4-31bgemma4 qwen3.5 qwen3.6 localllama

💡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.

Who should care:Developers & AI Engineers

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)

🦙Read original article on Reddit r/LocalLLaMA

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