Vibe Benchmarks
Vibe Benchmarks
July 2026
A quick round of informal benchmarking across a few local coding models. Nothing rigorous — just a few problems run through each model and seeing how they handled it. The goal was to get a sense of the trade-offs between quality, speed, and practical usability.
The models
- Qwen Coder Next — the best quality by a fair margin. It understood the problem, produced clean code, and generally got it right on the first try. The problem is that it’s too large and too slow. The latency was noticeable, and the larger context windows it supports actually work against it — with more context comes more tokens to process, and the slowdown compounds.
- Qwen 35B MoE — the best on balance. It matched Qwen Coder Next on most problems, was noticeably faster, and didn’t suffer from the same context-window bloat. For practical daily use, this is the sweet spot.
- Cohere North Mini — extremely fast. Almost instant responses. But it failed to solve several problems that Qwen 35B solved after one or two turns. North Mini kept going in circles — same wrong approach, repeated, unable to course-correct. Speed is great when it works, but not much use if it can’t actually solve the problem.
- Qwen 27B — too slow for my taste. I’d need to try it again under different conditions before forming a firm opinion. It had decent quality but the latency was a real drag.
- Gemma MoE — okay. Nothing wrong with it, but Qwen models were consistently higher quality. Gemma felt like it was trying its best but falling short on the harder problems.
TL;DR
Qwen Coder Next is the best. Qwen 35B MoE is the best on balance. Cohere North Mini is extremely fast but unreliable on actual problem-solving. Gemma MoE is fine but Qwen wins on quality. Qwen 27B needs another shot.