Best Local LLM for Coding in 2026: Developer's Guide

Best Local LLM for Coding at a Glance

Before diving into each model in detail, the table below provides a high-level comparison. 

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How We Ranked the Best Local LLMs for Coding

For this list, we selected some of the most capable models from a shortlist of over 50 candidates, then narrowed them down to the seven best options using the criteria below.

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• Benchmarks. The model must rank highly on HumanEval, SWE-bench Verified, LiveCodeBench, and Aider polyglot.
• Long-context. The model should have at least a 128K usable context window to enable it to perform better in large codebases.
• Tool and function calling. A must for Cursor, Aider, or Continue.
• Not too hard on the hardware. Has to run at Q4 on 24 GB VRAM or less.

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With the criteria covered, let’s dive into the list.

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What are the Best Local LLMs for Coding in 2026?

The list below covers our top picks in no particular order.

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Qwen3-Coder-Next — Best Local LLM for Coding Overall

Alibaba's February 2026 coder-optimised release is the strongest open-weight coding model you can run on your own hardware.

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Spec: 235B MoE (22B active), 256K context window

System requirements: 24 GB VRAM at Q4

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Qwen3-Coder-Next was released by Alibaba in February 2026. It’s a Mixture-of-Experts model that activates only a subset of its parameters per generation, allowing it to run on a single RTX 4090 even though the full weights take up 130 GB on disk.

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It scores 58.7% on SWE-bench Verified. This test gives the model a real bug report from an open-source project and asks it to produce a patch that passes the existing tests. For reference, Claude Sonnet 4 scores about 65% on it.

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Pros: best-in-class agentic coding and very long context window that utilizes the full depth.

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Cons: It’s a heavy model and slower than smaller models on this list at around 18–22 tokens per second on a 4090.

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If you'd rather set things up by hand, the full step-by-step is in our how to run AI locally guide.

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Qwen 3.5-27B — Best Local LLM for Coding on a Single GPU

An alternative to Qwen3-Coder-Next for developers who want the best performance on a single 24 GB card.

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Spec: 27B dense, 128K context window

System requirements: 18 GB VRAM at Q4

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Qwen 3.5-27B was released by Alibaba in late 2025. It’s a dense model, which means that every parameter fires on every token. This makes the model easier to set up on most hardware. The model scores 49.2% on SWE-bench Verified — about 9 points lower than Qwen3-Coder-Next on the same test, which is still very respectable.

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Pros: Runs on less VRAM and is optimized for over 20 programming languages

Cons: Behind Qwen3-Coder-Next on benchmarks.

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DeepSeek V3.2 — Best Local LLM for Reasoning-Heavy Coding Tasks

DeepSeek's late-2025 update remains the strongest open-weight model on algorithmic and math-heavy code.

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Spec: 671B MoE (37B active), 160K context window

System requirements: 24 GB VRAM

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DeepSeek V3.2 was released in late 2025 as an update to V3. Like Qwen3-Coder-Next, it's a Mixture-of-Experts model, but the active parameter count is higher at 37B and the total size is large enough that running it on a single 24 GB card requires offloading layers to system RAM.

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It scores 56.1% on SWE-bench Verified, slightly below Qwen3-Coder-Next, but it pulls ahead on LiveCodeBench — a benchmark built from competitive-programming problems collected after the model's training cutoff, which makes it harder to game.

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Strengths: It’s the best local coding model if your work is closer to LeetCode than landing pages.

Weaknesses: heavy to run.

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Llama 4 Scout — Best Local LLM for Coding with Massive Context

Meta's MoE release is the only local model that can fit a mid-sized codebase into a single prompt.

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Spec: 109B MoE (17B active), 10M context window

System requirements: 24 GB VRAM at Q4

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Llama 4 Scout is a Mixture-of-Experts model with a 10M-token context window — by far the largest in the open-weight space. It was released by Meta in 2025 — an older model, but still very capable. It's also multimodal, so it can read screenshots of error messages or architecture diagrams alongside code.

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It scores 47.3% on SWE-bench Verified, putting it behind the dedicated coding models on raw patch quality. But where it pulls ahead is the sheer size of the context window — it’s practically bottomless. You can paste an entire mid-sized codebase into one prompt and ask for cross-file changes, which no other local model can do.

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For context, Claude Opus 4.6 features a 1M context widow — 10× smaller than this local modal.

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Pros: 10M context window and multimodal input.

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Cons: mid performance as long as you purely look at the coding benchmarks, and in practice attention quality drops past ~2M tokens.

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Codestral 25.12 — Best Local LLM for Fast Code Completion

Mistral's coding-specialised model, refreshed in late 2025 and built around inline IDE completion.

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Spec: 22B dense, 64K context window

System requirements: 16 GB VRAM at Q4

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Codestral 25.12 was released by Mistral in December 2025. It's a dense model trained specifically for code completion — the kind of suggestion you wait for while typing in VS Code.

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It scores 42.0% on SWE-bench Verified, well below the agentic-focused models, but that's not what it was optimised for. On HumanEval-FIM, which tests in-line completion specifically, it sits at the top of the dense open-weight models.

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Pros: fast generation of code compilations that works on a 16 GB card.

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Cons: shorter context window.

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DeepSeek-Coder V3 (Distilled) — Best Local LLM for Coding on 16 GB VRAM

A distilled variant that brings most of DeepSeek V3.2's reasoning down to mid-tier hardware.

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Spec: 16B dense, 128K context window

System requirements: 12 GB VRAM at Q4

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DeepSeek-Coder V3 (Distilled) was released alongside V3.2 in late 2025. It was trained by distilling reasoning traces from the larger V3.2 model into a 16B dense student, which keeps a useful amount of the parent's chain-of-thought behaviour at a fraction of the size.

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It scores 40.5% on SWE-bench Verified — well below its parent, but the highest score on this list for any model that runs on 12 GB VRAM. It will run on a 4070 Ti or a 12 GB laptop GPU.

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Pros: best quality-per-GB on this list, runs on almost any modern GPU.

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Cons: ceiling is lower than the flagship models on hard problems.

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Gemma 4 26B A4B — Best Local LLM for Coding from Google

Google's MoE release prioritises tokens per second over raw benchmark scores.

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Spec: 26B total (3.8B active), 128K context window

System requirements: 14 GB VRAM at Q4

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Gemma 4 26B A4B was released by Google in early 2026. With only 3.8B parameters active per token, it generates tokens noticeably faster than the other models on this list — useful when you want a very fast chat experience.

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It scores 38.6% on SWE-bench Verified, the lowest of the top tier here, but it's also the fastest model on the list by tokens per second. For workflows where you want the suggestion to appear before you finish reading it, that tradeoff is often worth it.

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Pros: fastest local coding model.

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Cons: mid-to-low performance compared to flagship AI models.

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Best Local LLM for Coding by Use Case

Above, we covered the best local coding models overall, but the most powerful option isn’t always the best fit for your needs. For example, if you’re looking for autocompletion, a smaller, faster model can be more effective than a larger, slower one—even if the bigger model excels in context size and reasoning accuracy.

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With that in mind, here’s a breakdown of which model to choose based on your real-world use case.

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What is the Best Local LLM for Code Completion?

Answer: Codestral 25.12

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If you use your coding assistant like Copilot—typing in VS Code and waiting for suggestions—this model offers faster generation and is also easy on your hardware.

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Best Local LLM for Long Context

Answer: Llama 4 Scout

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Thanks to the 10M token context window, you can paste the entire code base of your project, and the model will be able to identify features across multiple files.

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Best Local LLM for Agentic Coding

Answer: Qwen3-Coder-Next

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This model was designed specifically for agentic loops, meaning it maintains high accuracy over long autonomous tasks.

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Best Local LLM for Algorithmic Programming

Answer: DeepSeek V3.2

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DeepSeek's reasoning model remains the most effective for maths-heavy and LeetCode-style work. It is the optimal choice for contest-style problems and data science.

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Best Local LLM for Frontend and Full-Stack Development

Answer: Qwen3-Coder-Next

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This model delivers exceptional performance with React, Vue and Tailwind. This means you can create better-looking, more polished and optimised front ends in fewer iterations.

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FAQ

What is the best local LLM for coding in 2026?

Qwen3-Coder-Next is the best overall local LLM for coding. It posts the highest HumanEval and SWE-bench Verified scores in the open-weight space and is purpose-built for agentic coding loops.

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What is the best local coding LLM for 16 GB VRAM?

Codestral 25.12 is the best fit for 16 GB VRAM — you’ll be able to run it comfortably at Q4 with room for context, and it will provide a very fast chat experience on that type of hardware.

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Is Qwen3-Coder-Next better than DeepSeek V3.2 for coding?

For most coding tasks, yes — Qwen3-Coder-Next leads on SWE-bench Verified and agentic benchmarks.

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Can I run a local coding LLM on a MacBook?

Yes, absolutely. An M4/M5 Max with 36 GB of unified memory can easily run Qwen 3.5-27B, Codestral 25.12, or Gemma 4 26B A4B. If you have an M4/M5 Pro, or a base M-series machine, you can run smaller distilled models. The easiest way to get started is by getting Atomic Chat, which is a native Mac application for running offline AI.

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Which local LLM has the longest context window??

Llama 4 Scout — it has a 10M-token context window. It's the only local model that can ingest a full mid-sized repository in a single prompt.

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Are local coding LLMs free to use?

Yes. Every model on this list is released under an open-weight license that permits free local use, including for commercial work.

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How do I connect a local LLM to VS Code or Cursor?

Run the model through Atomic Chat — it exposes an OpenAI-compatible endpoint. Just point your VS Code extension, (such as Continue) or Cursor's custom model settings at http://localhost:11434/v1.

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Wrapping Up: How to Get Started

Atomic Chat gives you the easiest way to to run any of the models on this list. It’s a local AI app that installs in one click and allows you to easily download any model from Hugging Face — then run it via a built-in chat UI and an exposed local API entirely offline.