Best Local LLM for Coding in 2026

The best local LLM for coding in 2026 is an open-weight model that fits on your GPU and works with your editor. For most developers, that means a Qwen coder build, Mistral's Devstral Small, a DeepSeek coder, or OpenAI's gpt-oss-20b. Each can run on a single consumer GPU and handle autocomplete, refactoring, and agentic edits offline.

This guide focuses on models you can realistically run at home. Server-class giants such as Qwen3-Coder-480B, DeepSeek's full models, and GLM-4.6 may top public coding leaderboards, but they're included only for context. Our recommendations fit GPUs with 12 GB to 24 GB of VRAM. For cloud and hosted options, see our separate guide to the best LLM for coding.

Running a coding model locally keeps your codebase private, eliminates API costs, and even works on a plane. The tradeoff is that you're responsible for the hardware and setup. Below, we rank the best options, break down their VRAM requirements, and cover the editor tools that turn them into practical coding assistants.

The Best Local Coding Models, Ranked by VRAM

The best local coding model for most developers is a Qwen coder build in a size that fits their GPU, it leads open coding benchmarks and ships in local sizes. The table ranks the top locally runnable options by the hardware they need.

The pattern to notice: a 24 GB card (an RTX 3090 or 4090) is the sweet spot for local coding. It runs a 32B coder at Q4, which is where open models get genuinely useful for real work.

How Local Coders Score on SWE-bench

SWE-bench Verified measures how often a model fixes a real GitHub issue, so it is the most reliable coding benchmark for agentic work. The catch is that the highest public scores belong to server-class models, not local ones.

Mistral's Devstral 2 (123B) reported 72.2%, and GLM-4.6 reported 68.2%, both need data-center GPUs. The locally runnable story is quieter but real: gpt-oss-20b reported 60.7% and runs in about 16 GB, and Qwen's and DeepSeek's mid-size coders land in the same range once quantized.

Treat published scores as a shortlist tool, not gospel, labs test under different setups, and a model that scores two points higher may feel worse in your editor. The bigger driver of daily productivity is the harness you wrap around the model.

The Editor Tools That Turn a Local Model into a Coding Assistant

A local model only becomes a coding assistant when you connect it to your editor through a harness. Serve the model with Ollama or LM Studio, then point one of these tools at it.

Cline and Continue are the two most popular open-source VS Code extensions for local models, both speak to an Ollama endpoint and support autocomplete plus agentic, multi-file edits. Aider is a terminal-based pair programmer that works well with local coders for larger refactors. The Zed editor also has built-in support for local model endpoints.

In our own automation work we run coding models through local endpoints for privacy-sensitive tasks, and the recurring lesson is that harness quality and context handling decide the experience more than a two-point benchmark gap between models. Pick a model that fits your GPU, then invest in the harness.

• Cline (VS Code), agentic edits, reads and writes across files, connects to Ollama.
• Continue (VS Code / JetBrains), inline autocomplete plus chat against a local model.
• Aider (terminal), git-aware pair programming, strong for multi-file refactors.
• Zed, a fast editor with native local-model endpoint support.

Context Window: Why It Matters for Real Codebases

For coding, a model's context window matters as much as its raw score, because it sets how much of your code the model can see at once. A model that fits your whole file, its imports, and the related modules gives far better answers than one that only sees a snippet.

Most current local coders handle long context well, Qwen's coder builds and Yi-Coder reach into the hundreds of thousands of tokens, enough for large files and multi-file edits. The practical limit is your VRAM: a longer context uses more memory, so on a smaller card you trade some context length for the ability to load the model at all.

A useful rule for real repositories is to feed the model the current file plus the handful of files it directly depends on, not the entire project. Harnesses like Cline and Aider do this automatically by selecting relevant files, which is why the harness matters as much as the raw context number.

Fine-tuning a Local Coder on Your Own Code

One advantage a local coder has over a cloud API is that you can fine-tune it on your own codebase, so it learns your conventions, your internal libraries, and your patterns. Because you hold the weights, this stays entirely private.

For most teams full fine-tuning is overkill. A lighter approach, retrieval over your repository, where the harness feeds the model relevant snippets at query time, captures most of the benefit with none of the training cost, and it updates instantly as your code changes. Start there before considering a fine-tune.

When we set up private coding assistants for engineering teams, retrieval over the repo plus a well-chosen off-the-shelf model almost always beats a rushed fine-tune, and it keeps the whole pipeline on the team's own hardware.

Which Local Coding Model Should You Pick?

Pick by your GPU first, then your task. If you have a 24 GB card, run a 32B Qwen coder for the best all-round local experience. If you have 12–16 GB, gpt-oss-20b or a 14B Qwen coder is the sweet spot. If you only have 8 GB, a 7B Qwen or DeepSeek coder still handles autocomplete and small edits well.

For agentic, multi-file work, where the model plans and edits across a repo, Devstral Small 2 and gpt-oss-20b are built for it. For pure fast autocomplete, Codestral is excellent for personal projects. For anything you ship commercially, stay on the Apache 2.0 and MIT models to avoid license trouble.

Whatever you choose, confirm it fits before you download it with our local AI hardware calculator, and see the full field in our best local LLMs ranking.