Best LLMs for OpenCode - From Qwen 3.5 to Gemma 4, Tested Locally
gemma llama qwen
| Source: Mastodon | Original article
A new hands‑on benchmark released on glukhov.org has mapped the performance of today’s leading open‑source large language models when used with OpenCode, the AI‑driven coding assistant that has quickly become a staple for developers seeking locally hosted alternatives to cloud‑only services. The author tested Qwen 3.5 (0.5 B‑72 B variants), Google’s Gemma 4 (9 B and 27 B), and Meta’s Llama 4 (8 B‑70 B) on both Ollama and llama.cpp, then compared the results with the free cloud tier of OpenCodeZen.
Qwen 3.5 27 B in the IQ3_XXS quantisation emerged as the fastest model for generating complete Go projects, but the migration‑map checks revealed a “slug‑mismatch” rate of more than 6 000 % in two runs, and the IQ4_XS variant omitted page slugs altogether. Gemma 4’s 9 B version delivered steadier accuracy on smaller snippets, while the 27 B model matched Qwen’s speed but required substantially more RAM. Llama 4 showed the best context‑length handling (up to 512 K tokens) but lagged on raw coding throughput.
Why it matters: the study demonstrates that high‑quality code generation is now viable on consumer‑grade hardware, giving developers control over data privacy and operating costs. It also highlights a trade‑off that has been invisible in cloud‑only benchmarks—quantisation can cripple reliability even when raw speed looks impressive. The findings dovetail with our earlier coverage of Alibaba’s Qwen‑3.5 reasoning boost (5 Apr) and Google’s Gemma 4 performance on a 48 GB GPU (5 Apr), confirming that the same models that excel in reasoning also dominate local coding workloads.
What to watch next: the OpenCode team plans a version‑2 release with tighter integration for Ollama’s upcoming pre‑release, which could smooth out the slug‑generation bugs. Model creators are already teasing improved low‑bit quantisation pipelines, and the community is expected to publish follow‑up “real‑world” tests on multi‑modal tasks later this quarter. Keep an eye on how these refinements reshape the balance between local autonomy and cloud convenience for AI‑augmented development.
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