Best local LLM for coding in 2026, by hardware

How good is local coding, really?

Only one model in our finder has a Code Arena rank so far: Qwen3.8 27B. It sits at #27 of the 53 models we price on Code Arena, just behind Claude Haiku 5.5 and ahead of MiniMax-M3.1 Flash, Gemini 3.8 Flash and GPT-6 Luna. The top of the board is Claude Fable 5.2, which scores far higher. In plain terms: a 27B model on your own GPU codes about as well as the small, cheap cloud models, not the flagships.

That is fine for autocomplete, explaining code, writing tests and small edits, and the code never leaves your machine. For long agent runs across a big repo, a frontier cloud model still wins clearly.

If privacy is not the reason you want local, compare the bill first. The Code Arena value picks right now are MiMo-V2.6 Flash ($0.17 per 1M tokens blended), GLM-5.3 Flash ($0.24 per 1M tokens blended) and DeepSeek V4.1 Flash ($0.53 per 1M tokens blended). See the best model for coding page and the cheapest LLM API guide.

Mixture of experts vs dense for coding

Speed on local hardware depends on how many weights each token has to read. A mixture-of-experts model such as Qwen3 Coder 30B A3B or Recursive Flint only reads its active slice (about 2.5B to 3.3B weights) per token, so it stays fast even on a laptop. A dense 27B model reads all 27B every token, which is why the same Mac that runs Flint at 100+ tokens per second runs Qwen3.8 27B at around 11 to 17 (our estimates).

The trade-off is memory. MoE models still need all their weights loaded, so a 40B MoE needs more room than a 27B dense model. If your machine has the memory but modest bandwidth (most Macs, CPU-only PCs), pick MoE. If you have a fast 24GB or 32GB GPU, dense models are quick enough, and the dense Qwen3.8 27B is the only finder model with a Code Arena rank.

Every coding model in our finder, smallest first

Using a local model with your editor or coding agent

Ollama and LM Studio both run a local server with an OpenAI-compatible API, so most editors and agents that let you set a custom endpoint can use it. Pull the model and quant from the tables above, then point the tool at the local server.

Coding agents send long prompts (whole files, tool output), so give the model a bigger context window than you would for chat, and expect it to use more memory than the download size. If a model is close to your memory limit, drop to the next smaller pick rather than going below Q4.

Model sizes and licenses come from each official model card, linked from every model's requirements page.