Best Local LLM for Coding in 2026: A Comprehensive Guide

Updated October 7, 2026: Qwen3.8 27B is now our main recommendation. The original hands-on results remain attributed to the models we tested.

In this article, we're talking about the best local coding models, but if you also want to compare how local LLMs stack up against cloud flagships like Claude, GPT-5.5, or Gemini, read about the best cloud and open source coding models →

TL;DR

• The best Local coding models are now almost as good as cloud flagships. The best open-weight models score around 80% on SWE-bench Verified, which is comparable to Opus 4.7 or GPT 5.2 Codex performance.
• Qwen3.8 27B is our current all-round pick for local coding. We base this recommendation on Qwen's published evaluations and available local builds; we have not run it through this article's four-model comparison. For a speed-focused alternative, Qwen3-Coder 30B reached 220 tok/s in that original test.
• Laguna S 2.1 is an option for high-memory machines. Poolside's 118B MoE model has 8B active parameters and targets long-running agentic coding. Its memory requirements depend on the quantization you choose.
• Choose a local coding model that your machine can run, not the best one there is. The 200B+ parameter models need special hardware to run at usable speeds. Choose models that are 80B and less and use 3–4x quantization to compress them further. If you have under 30 GB of vram (or unified memory for mac users), choose 27B–30B models.
• Use Atomic Chat to run the best local coding models in one click. Atomic Chat is an open-sourced app that lets you download open weight models from Hugging Face and run them on your own hardware. It features an OpenAI-compatible API, so you can easily plug the models into any IDE or Claude Code.

Why use a local coding model in 2026?

Open-weight models now deliver performance on the level of previous-generation proprietary flagships — competitive for real day-to-day software development, while running entirely on local hardware. Running an open source LLM locally comes with all the benefits of local AI:

• Privacy — your code and prompts never leave your device. For proprietary code, client work, or anything under an NDA, a local LLM is the only option that keeps your data private and off a third-party server.
• No cost — open-weight models are free to download and there's no per-token API bill, no matter how much you generate.
• Offline access — use the model anywhere, even with no internet, with no rate limits.

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Note: While it's exciting to discuss the best local models there are (in absolute terms), these 200B–300B+ models aren't realistic to run on everyday hardware. The general rule is that you need about 1 GB of VRAM per one billion parameters, so some of these models require over 300 GB of GPU memory.

That said, in early 2026 there's been a massive quality jump in medium to small models, and 24–30B models now give you 80–90% of the performance that just a few months ago was considered flagship-level.

The original hands-on comparison covers Qwen3-Coder-Next 80B, Qwen3-Coder 30B, Gemma 4 26B A4B, and Qwen3.6 27B. We added Laguna S 2.1 and Qwen3.8 27B later, without results from the same two-task comparison. Below, we distinguish our measured results from vendor-reported evaluations.

Testing methodology

When evaluating the best coding LLMs, we considered:

1. Public benchmarks
2. Results across multiple coding tests

For benchmarks, we took into account:

• SWE-bench Verified — the model has to fix real GitHub issues
• LiveCodeBench — the model has to solve programming problems that were released after the knowledge cutoff date
• Terminal-Bench — the model has to run agentic command-line tasks

For the second part of the test, we ran all four models in Atomic Chat on the same hardware and gave each the same two prompts:

1. Write a playable Snake game and play it until you lose
2. Build a physics simulation with bouncing balls

We recorded the generation speed in tokens per second, how many tokens each model spent, and whether the result ran correctly. Here's a video showing how they handled a physics simulation:

And here's how they handled creating and playing a Snake game:

‍The best local LLMs for coding

These six entries include our current recommendation and the earlier models. We retain Qwen3.6 27B as a reference for the original test results.

• Qwen3.8 27B
• Qwen3-Coder-Next 80B
• Laguna S 2.1
• Qwen3-Coder 30B
• Gemma 4 26B A4B
• Qwen3.6 27B

Here are the core specs for all six models. A missing speed result means that model was not part of our original four-model comparison.

Keep reading for a more detailed overview of each model, benchmark results, and how it held up in our own testing.

Qwen3.8 27B: our current pick

Qwen3.8 27B is our current all-round recommendation for local coding. Qwen reports better coding results than its Qwen3.6 27B predecessor in its published evaluations.

The official model card reports 61.7 on SWE-bench Pro, 73.0 on Terminal Bench 2.1, and 90.3 on LiveCodeBench v6. These are vendor evaluations, not results from our Snake and physics tasks. SWE-bench Pro is a different benchmark from SWE-bench Verified.

The AtomicChat AD-Q4_K_M build has a 17.1 GB weight file. Context cache and runtime allocations need additional memory. Use our Qwen3.8 27B local setup guide to choose a quantization and context size for your hardware.

Testing status: We have not measured Qwen3.8 27B in this article's speed and two-task comparison. The 47 tok/s and Snake results below belong to Qwen3.6 27B.

Qwen3-Coder 30B

What is it? Qwen3-Coder 30B is a coding model from Alibaba's Qwen team, part of the Qwen3-Coder family and distributed under the Apache 2.0 license. This is a Mixture-of-Experts model with 30.5 billion parameters and about 3.3 billion active per token, drawn from 128 experts with 8 routed per pass. It has a 256K-token context window that extends to 1M tokens with YaRN.

Here are the key Qwen3-Coder 30B specs at a glance:

According to the Qwen team, 30B-A3B was tuned specifically for agentic coding — it's a very efficient model. In our own testing it finished the physics task in about 1,840 tokens — the most token-efficient result of the four models.

Qwen3-Coder 30B Pros:

• Fastest model in our test at 220 tokens per second — roughly 40× the 80B
• Most token-efficient
• Fits a single 24 GB GPU

Qwen3-Coder 30B Cons:

• No thinking mode

When to choose Qwen3-Coder 30B: Choose it when interactive generation speed is your priority. It was the fastest of the four models in our original test at 220 tok/s. We have not compared its speed with Qwen3.8 27B under the same conditions.

Qwen3-Coder-Next 80B

What is it? Qwen3-Coder-Next is another open-weight coding model released by Alibaba's Qwen team in February 2026.

Yes, we're including multiple Qwen models here, but that's because the Alibaba team develops some of the best local AI models in the world.

Qwen3-Coder-Next 80B uses a Mixture-of-Experts architecture with 80 billion total parameters, of which only about 3 billion are active on any given token, drawn from 512 experts with 10 routed and 1 shared per pass.

The model was built for agentic coding and it supports a native 256K-token context window. Because only 3B parameters are active per token, Qwen reports performance comparable to models with 10–20× more active compute.

Here are the key Qwen3-Coder-Next 80B specs at a glance:

On the coding benchmarks Qwen publishes for the model, Qwen3-Coder-Next 80B scores as follows:

Qwen3-Coder-Next 80B Pros:

• Very high reasoning quality
• 256K context window

Qwen3-Coder-Next 80B Cons:

• Needs roughly 45 GB of VRAM or unified memory
• Slow on consumer grade hardware

When to choose Qwen3-Coder-Next 80B: If you have a powerful machine — roughly 45 GB of VRAM or a high-memory Mac — and want the best reasoning performance. Or, if you have less powerful hardware, if you're ok to give it a long-horizon task and leave it to run overnight.

Laguna S 2.1

What is it? Laguna S 2.1 is an open-weight coding model from Poolside, released on July 21, 2026 under the OpenMDW-1.1 license. It is a Mixture-of-Experts model with 118 billion total parameters and about 8 billion active per token, purpose-built for agentic coding and long-horizon software engineering work.

The model supports a context window of up to 1M tokens and runs in two modes — standard and thinking. Thinking is on by default and sets its own compute budget; on Terminal-Bench 2.1 it lifts the score from 60.4% to 70.2%.

Here are the key Laguna S 2.1 specs at a glance:

On the benchmarks Poolside publishes, Laguna S 2.1 scores as follows:

Poolside reports these results using its own evaluation setup; they do not establish a like-for-like ranking against the other models in this guide. Poolside trained the model on 409k agentic and non-agentic environments, including 83k terminal tasks, and has demoed it building a working browser engine in vanilla JavaScript in about 50 minutes.

Laguna S 2.1 Pros:

• The strongest agentic coding results of any open model in its weight class
• Only 8B active parameters, so generation is fast once the model fits in memory
• 1M-token context window

Laguna S 2.1 Cons:

• Needs roughly 67–96 GB of VRAM or unified memory even at low-bit quantization
• Occasionally produces malformed JSON in nested tool calls

When to choose Laguna S 2.1: If you have a 96–128 GB Mac Studio, an NVIDIA DGX Spark, or a multi-GPU workstation and want the strongest open-weight agentic coder you can run locally. On more modest hardware, look at the smaller Laguna XS 2.1 (33B total / 3B active) instead.

Gemma 4 26B A4B

What is it? Gemma 4 26B A4B is an open-weight Mixture-of-Experts model from Google DeepMind, released on April 2, 2026 under the Apache 2.0 license. It has 26 billion total parameters but only about 4 billion active per token, drawn from 128 small experts with 8 firing per pass. That means it runs at the speed and cost of a 4B model while drawing quality from the full 26B pool.

It supports a 256K context window through sliding-window attention, which keeps memory from ballooning as the context grows. Of the four models here, it's the easiest to fit on a smaller GPU.

Here are the key Gemma 4 26B A4B specs at a glance:

On the coding benchmarks Google publishes, Gemma 4 26B A4B scores as follows:

Note: Gemma 4 score is noticeably lower than Qwen models on SWE-bench Verified — it's less effective on large repository tasks.

In our tests, it successfully built both the Snake game and the bouncing-balls simulation. It also ran quickly, generating about 136 tokens per second. With Atomic Chat's Multi-Token Prediction enabled, throughput increased by up to 3×.

Gemma 4 26B A4B Pros:

• Runs from roughly 12 GB of VRAM
• Fast, with up to a 3× speed boost in Atomic Chat
• Strong on competitive coding, math, and science benchmarks

Gemma 4 26B A4B Cons:

• Weaker on SWE-bench Verified / agentic repository tasks
• The most verbose model in our test — it spent ~3,724 tokens on the Snake task, far more than the 30B for comparable output

When to choose Gemma 4 26B A4B: if your GPU is has 12–16 GB of VRAM or you want very fast inference.

Qwen3.6 27B

What is it? Qwen3.6 27B is the first dense open-weight model in the Qwen3.6 family — and another Qwen family model we're including here. It was released by Alibaba on April 22, 2026. Qwen3.6 27B is a dense 27-billion-parameter model, which means that every single parameter is active on every token — this makes it harder to run, but a little bit more powerful given you have the hardware (at least in theory — more on that below).

Notably, Qwen3.6 27B is a multimodal LLM — you can send it text, images, and videos and it will understand them. To work across large codebases, the model has a 256K context window that extends to ~1M tokens. In terms of performance, it beats a much larger 397B Qwen3.5 MoE on agentic coding and roughly matches Claude 4.5 Opus.

Here are the key Qwen3.6 27B specs at a glance:

On the coding benchmarks Qwen publishes, Qwen3.6 27B scores as follows:

In our testing this model created and played the best Snake game of the four, reaching a high score of 80 without crashing, but to our surprise it was the only one that failed the physics task — the bouncing-balls simulation had very chaotic movement and unnatural acceleration. Because all 27B parameters run on every token, it was also slower than the MoE models at 47 tokens per second.

Qwen3.6 27B Pros:

• Published vendor coding evaluations
• Accepts text, image, and video input

Qwen3.6 27B Cons:

• Slow and difficult to run comopared to MoE models
• Failed our physics simulation test

Where Qwen3.6 27B fits: We retain it as the earlier dense model from our original comparison. For a new setup, Qwen3.8 27B is our current recommendation. The speed and game results above remain results for 3.6.

Honorable mentions

This list wouldn't have been complete if we didn't talk about the best local coding models in 2026 — even if they're not realistic to run at home for most people. Here are our honorable mentions:

DeepSeek-V4-Pro

DeepSeek-V4-Pro is an open-weight Mixture-of-Experts model from DeepSeek, released on April 24, 2026. It has 1.6 trillion total parameters with 49 billion active per token and a 1M-token context window. It scores around 80.6% on SWE-bench Verified, the highest public result outside the proprietary labs. At that size it needs 80 GB or more of VRAM, which makes it a data-center or multi-GPU model rather than a home one.

GLM-5.1

GLM-5.1 is an open-weight model from Z.AI (formerly Zhipu AI), released on April 7, 2026 under the MIT license. It is a 754-billion-parameter Mixture-of-Experts model with 40 billion active parameters and a roughly 200K-token context window, built for long-horizon agentic tasks. It ranks near the top of coding leaderboards, though its headline scores are largely vendor-reported, so the exact numbers are best treated with caution until independent results land.

Kimi K2.7-Code

Kimi K2.7-Code is an open-weight coding model from Moonshot AI, released in June 2026. It is a Mixture-of-Experts model with 1 trillion total parameters and 32 billion active per token. Moonshot reports a 21.8% gain over K2.6 on its own Kimi Code Bench v2 and about 30% lower reasoning-token usage, but it did not publish SWE-bench Verified or Pro scores at launch, and no independent third-party benchmarks exist yet — so the reported gains are worth watching but not confirmed outside the vendor.

MiMo-V2.5-Pro

MiMo-V2.5-Pro is an open-weight Mixture-of-Experts model from Xiaomi, released on April 22, 2026. It has 1.02 trillion total parameters with 42 billion active per token and a 1M-token context window. Xiaomi reports strong agentic results (around 78.9% on SWE-bench and 68.4% on Terminal-Bench), placing it among the more capable open models, though its size again puts it beyond home hardware.

Other models worth knowing

A few older or more specialized models are also worth a mention:

• DeepSeek V3.2 — for reasoning-heavy work
• Devstral 2 — Mistral's dedicated agentic coder
• Codestral — for fast fill-in-the-middle autocomplete
• Llama 4 Scout — for its 10M-token context window, the largest of any open model
• StarCoder 2 — for a model trained on fully auditable, openly licensed data

How to run the best local LLMs for coding

The best local LLM for coding is only useful if you can plug it into an agent or IDE. The setup below shows how to run local coding models with any OpenAI-compatible toolchain, whether you're after a full agentic workflow or just want a private model for vibe coding.

We used Atomic Chat as the runtime, but the same setup works with any local inference server that exposes an OpenAI API, including Ollama and LM Studio.

Setup steps

1. Install a local LLM runtime (e.g. Atomic Chat).
2. Download a coding model that fits your GPU.
3. Start the local server.
4. Connect your coding agent to the API endpoint.

Connect a coding agent

Atomic Chat has an OpenAI-compatible endpoint. Local runtimes expose this at:

You can plug this into tools like OpenCode, Goose, or Kilo Code:

Example OpenCode config:

• Base URL: http://127.0.0.1:1337/v1
• Adapter: @ai-sdk/openai-compatible
• Model name: must match the one exposed by the local server

List available models:

Network access (optional)

By default, the server runs locally on 127.0.0.1. Set 0.0.0.0 if you want to access it from another machine on your network.

Tools and speed

Atomic Chat also has built-in Model Context Protocol (MCP) support, so you can connect multiple MCP servers to give the model your own tools, file access, and web search, with an in-app log viewer for every tool call.

It also includes two techniques that speed up generation on the models in this guide:

• Multi-Token Prediction (MTP) — a speculative decoding method that adds a 30–70% throughput boost on supported models, up to 3× on Gemma 4.
• DFlash — a block-diffusion decoding method that runs up to 6× faster on Qwen 3.6, Gemma 4, and Kimi K2.5.

With these enabled, the models can run faster than the raw token-per-second figures we measured.

How much VRAM do you need to run local coding models?

As a rule of thumb, you need roughly 1 GB of VRAM per 1 billion parameters at 4-bit quantization. For example:

• 7B models need about 4–6 GB — these run on an 8 GB VRAM GPU and are a good fit for Python and single-file coding tasks.
• 13B models need about 8–10 GB, comfortable on a 16 GB VRAM card.
• 27B–34B models need about 16–24 GB — this is the performance sweet spot for a local coding model, and where Qwen3-Coder 30B, Qwen3.6 27B, and Gemma 4 all sit.
• 70B-plus models need 40 GB and up, which usually means multiple GPUs.

Mixture-of-Experts models complicate the math a little: a model like Qwen3-Coder-Next 80B activates only 3B parameters per token, so it generates with the compute of a small model, but all 80B weights still have to be loaded — memory tracks the total size, while speed tracks the active size.

Use these hardware starting points for the six models. For Qwen3.8 27B, budget for the context cache and runtime in addition to the weight file.

A Mac has an advantage here: because its unified memory is shared between CPU and GPU, a 64 GB Mac Studio can load the 80B model that would otherwise need two GPUs, but a discrete GPU will have faster inference because of higher bandwidth.

What is Quantization? Quantization is a way to shrink the model weights from 16-bit down to 4-bit or 8-bit, trading a small amount of quality for a large drop in memory.

The common Quantization formats are GGUF (used by Ollama, LM Studio, and Atomic Chat, and the most flexible since it splits work across CPU and GPU), and GPTQ and AWQ (GPU-only 4-bit formats). For most people, a Q4_K_M GGUF is the right starting point — it's the standard quality-to-size sweet spot, and it's what our VRAM figures above assume.

FAQ

What is the best local LLM for coding in 2026?

Qwen3.8 27B is our current all-round pick, based on Qwen's published evaluations and available local builds. We have not run it through this article's hands-on comparison. Qwen3-Coder 30B remains the fastest model in our original test.

Can you run a coding LLM locally for free?

Yes. The models themselves are open-weight and free to download from Hugging Face, and the software to run them — Atomic Chat, Ollama, LM Studio — is free too. The only cost is the hardware you already own; there's no subscription and no per-token charge.

How much VRAM do I need to run a local coding model?

At 4-bit quantization, a 7B model needs about 4–6 GB, a 13B model about 8–10 GB, and a 27B–34B model about 16–24 GB. The strongest models that fit a single consumer GPU sit in that 24 GB tier, so a card like an RTX 4090 or 3090 covers most of them.

Is a local LLM good enough to replace ChatGPT or Claude for coding?

For most tasks, close. The best open-weight models now score around 80% on SWE-bench Verified versus roughly 90–95% for the top proprietary models, so the very hardest agentic problems still favor the cloud. For everyday generation and refactoring — especially on private code — a local model is enough.

What's the best local coding model for a single 24 GB GPU?

Start with a Q4 build of Qwen3.8 27B and leave memory for the context cache and runtime. Check that it fits at the context length you need; fitting the weight file alone is not enough. Qwen3-Coder 30B is the speed-focused alternative from our original comparison.

What's the best local coding model for 8–12 GB VRAM?

Gemma 4 26B A4B runs from about 12 GB at 4-bit, which makes it the most capable model in this guide for a smaller card. Below 12 GB, drop to a 7B–13B coding model such as a DeepSeek-Coder Lite variant.

Are local LLMs private — does my code stay on my machine?

Yes. A local model runs entirely on your computer, so nothing is sent to an external server the way it is with Copilot, ChatGPT, or Claude. For proprietary code, client work, or anything under an NDA, that's the main reason to run locally.

What's the difference between a 30B MoE and a 27B dense model for coding?

A Mixture-of-Experts model like Qwen3-Coder 30B activates only a few billion parameters per token, so it generates fast while still loading the full model into memory. A dense model like Qwen3.6 27B runs every parameter on every token, which is slower but tends to be more consistent on hard reasoning. In our test the MoE model was about 5× faster; the dense model scored higher on benchmarks.

Do local models support agentic coding and tool use?

Yes. Qwen3-Coder 30B and Qwen3-Coder-Next 80B are tuned specifically for agentic work, and running them through Atomic Chat's Model Context Protocol support lets them call your own tools and act on local files. That's what turns a chat model into an agent that can act on your codebase.

How do I connect a local model to a coding agent or IDE?

Run the model in Atomic Chat, which exposes an OpenAI-compatible server at http://localhost:1337/v1. Any agent or IDE plugin that speaks the OpenAI API — OpenCode, Goose, Kilo Code — can point at that address and use your local model in place of a cloud one.

What is the best local LLM for coding with Ollama?

Qwen3-Coder 30B is the best local coding model to run with Ollama on a single 24 GB GPU — it's fast and fits comfortably at Q4. Ollama, LM Studio, and Atomic Chat all run the same open-weight models, so the right pick depends on your VRAM rather than the runtime.

Can you fine-tune a local coding LLM on your own code?

Yes. Because these are open-weight models, you can fine-tune them on your own codebase to match your stack and conventions — something you can't do with a closed API model. Fine-tuning a 27B–30B model is realistic on a single high-VRAM GPU, though most developers get enough value from the base model plus a long context window.