Best LLMs for OpenCode - Tested Locally

The thread starts on March 21st, when a developer published field notes on Dev.to comparing LLMs running inside OpenCode, the terminal-based coding assistant built on a simple premise: your source code stays on your machine, full stop. OpenCode attracts a particular kind of user — someone who has already made the privacy or sovereignty decision and just needs to know which model actually helps them write better code. That first post circulated quietly, the way useful-but-niche things do. Then on April 20th, the conversation sharpened considerably. A LocalLLaMA user posted a proper benchmark: 21 local LLMs tested on a MacBook Air M5, scoring for both code quality and inference speed. That's the combination that matters for daily use — a model that writes clean code but takes forty-five seconds per response will quietly get abandoned. The M5 benchmark gave the community something concrete to argue about, and argue they did. By April 26th, two follow-up threads had appeared in quick succession: one asking whether Unsloth's fine-tuned models live up to their reputation, and another from a developer who concluded they do — but who prefers official quantizations anyway, citing consistency and reproducibility over marginal quality gains. That last post is the most revealing signal in the bunch. It suggests the local LLM community is maturing past pure performance chasing toward something more considered: reliability, predictability, the boring virtues that matter when a tool is part of your actual workflow.

If confirmed, here is what this means. The market for local coding assistants is no longer a hobbyist experiment — it's a functioning ecosystem with enough users to generate meaningful comparative data across hardware, models, and quantization strategies. Developers who care about code privacy now have a genuine alternative to cloud-based copilots, not a compromised one. The M5 benchmarks matter beyond Apple enthusiasts because they establish what's possible on a mid-range consumer machine, which sets expectations and lowers the barrier for anyone considering the switch. The Unsloth debate is a second-order signal worth watching: if community-fine-tuned models can match or beat official releases on coding tasks, it puts pressure on model labs to either improve their base quantizations or engage more directly with the fine-tuning ecosystem. And the developer who chose official quantizations for consistency reasons is pointing at something the benchmarks don't capture — that workflow integration and predictable behavior are often worth more than peak benchmark scores. That's a sign of a community thinking like engineers rather than enthusiasts.

Watch for formal evaluations or independent reproductions of the M5 benchmark results, and for OpenCode itself to publish any model recommendations — if the tool's maintainers start weighing in on which models pair best with their system, that would crystallize the community consensus into something more authoritative.

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