The Best Local LLMs for Coding (2026)
By LocalLLMGear Editorial · Editorial Team · Updated 2026-06-29
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If you write code, a local LLM is one of the most useful things you can run on your own machine: autocomplete, refactors and “explain this function” — all private, offline and free of per-seat fees. But not every model is good at code, and the right pick depends entirely on how much VRAM you have. This is the honest rundown of the best open code models in 2026 and how to actually use them.
The 30-second answer: As of 2026, the Qwen Coder family is the strongest all-round open code model for local use — pick the biggest size that fits your GPU. DeepSeek Coder is a great alternative, and the Code Llama family is still a dependable baseline. Have ~24 GB VRAM? Run a 32B coder. Got 8 GB? A 7B coder is genuinely useful. Releases move fast, so check the latest versions before you commit.
What makes a model good at code (not just chat)
A “coding” model is one that’s been trained heavily on source code and related data, so it’s better at the things programmers actually need: completing a function from context, following a strict output format, reasoning about types and APIs, and not hallucinating library calls. General chat models can write code, but dedicated code models tend to be more reliable at fill-in-the-middle completion and at producing diffs an editor can apply cleanly.
Two things matter most when you pick one to run locally: the model’s size (bigger generally writes better code) and whether it fits in your VRAM at a sensible quantization. If it doesn’t fit, it either won’t load or it spills into system RAM and crawls.
The best open code models in 2026
These are established, widely-used families. Within each, you’ll find several sizes — choose by VRAM (see the table below). All of these run great through Ollama or LM Studio.
- Qwen Coder family — the current go-to for local coding for a lot of people. Comes in a wide range of sizes, so there’s a version for almost any GPU, and the larger ones are genuinely strong at multi-language code and following instructions. Start here if you’re unsure.
- DeepSeek Coder — another excellent open code family with a strong reputation for code quality and fill-in-the-middle completion. A great alternative or A/B partner to Qwen. (New to it? See Run DeepSeek locally once it’s set up.)
- Code Llama family — Meta’s code-specialized models. Not the newest, but rock-solid, extremely well-supported by tooling, and available in sizes from small to large. A safe, boring-in-a-good-way baseline.
There are general models that code well too (Llama and Mistral derivatives, plus larger “instruct” models), but if coding is your main use, a dedicated code model usually gives you more per gigabyte of VRAM.
Which size for your hardware?
The single most important decision. Here’s the rough mapping for 4-bit quantized code models — approximate, label it as such, and verify against the exact model you download:
Picking a local coding model by VRAM (4-bit quantized, approximate)
| GPU / Option | VRAM | Best for |
|---|---|---|
| 7B coder (e.g. Qwen / DeepSeek 7B) | 6–8 GB | Laptops & entry GPUs — autocomplete, small edits |
| 13B–14B coder | 10–12 GB | Mid-range cards — noticeably better reasoning |
| 32B–34B coder (Qwen Coder 32B, Code Llama 34B) | 20–24 GB | RTX 3090/4090 — strong, near-frontier local coding |
| 70B class (general models that code) | 40–48 GB | Dual-GPU rigs — best local quality |
Rule of thumb: run the largest model that fits comfortably in your VRAM with room for context. A 32B coder on a 24 GB card is the sweet spot for serious local coding in 2026 — it’s close enough to the big cloud models for day-to-day work. If you’re choosing or upgrading a card, our Best GPU for local LLMs guide covers exactly which VRAM tier unlocks which models.
How to actually use them in your editor
A model on its own is just a chat box. The payoff comes from wiring it into your editor so it works where you already do.
- Pull the model. The fastest path is Ollama — one command and you have a local model
plus an API your tools can call. Our complete Ollama guide
walks through install, pulling a model, and the local API at
localhost:11434. - Connect a VS Code extension. Open-source extensions like Continue and Cline plug a local model into VS Code for inline autocomplete, chat-with-your-code, and multi-file edits — a private alternative to cloud copilots. You point them at your Ollama (or LM Studio) endpoint and you’re done.
- Pick the right model per task. Many people run a small fast coder for autocomplete and a larger one for chat/refactors. Two models loaded at once doubles VRAM use, so watch your memory.
For the full setup — extensions, endpoints and configuration — see our software guides, which cover the tools that turn a raw model into a real coding assistant.
So which one should you pick?
- Most people: start with a Qwen Coder at the largest size your VRAM allows.
- Want an alternative or a second opinion: add DeepSeek Coder and compare on your own code.
- Want maximum stability and tooling support: the Code Llama family is a safe bet.
- 8 GB GPU or a laptop: a 7B coder is the move — modest, but real and private.
A capable model and an editor extension genuinely cover a large chunk of what paid cloud copilots do, for free and offline. The gap to the very best cloud models narrows every release, and on a 24 GB card the difference is small for everyday work.
If you want to get more out of these tools — prompting, debugging, and building on top of local models — a structured course shortcuts a lot of trial and error:
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There’s no single “best” — there’s the best that fits your hardware. As of 2026, Qwen Coder is the strongest all-round local code family for most people, DeepSeek Coder is a top-tier alternative, and Code Llama remains a dependable baseline. Match the size to your VRAM, wire it into VS Code via Ollama, and you have a private coding assistant that costs nothing per month. The model landscape moves quickly, so check the latest releases before you download — but those three families are where to start.
Not sure your GPU is up to the size you want? Start with Best GPU for local LLMs.