Best Local LLMs for Coding in 2026

Editor's pick

Qwen3-Coder-30B-A3B is the model most teams should reach for first when they want a capable coding assistant that runs entirely on their own hardware. It is a mixture-of-experts design with 30.5 billion total parameters but only 3.3 billion activated per token, which is the architectural trick that lets it post strong coding results while fitting comfortably on a single 24GB consumer GPU at a usable speed. Its model card lists a 262,144-token native context window, extensible toward a million tokens with YaRN, so it can hold a large slice of a repository in a single prompt rather than forgetting the file you opened ten edits ago. Qwen tuned it specifically for agentic coding and browser-use loops rather than one-shot snippet generation, which matters in 2026 because the useful work has shifted from autocomplete toward multi-turn tasks: read an issue, plan a fix, edit several files, run tests, iterate. The license is the quiet headline — it ships under Apache 2.0, so you can self-host it, fine-tune it and ship it inside a commercial product with no vendor sign-off. The honest weakness is that the 30B-A3B model card describes its strengths qualitatively rather than publishing the SWE-bench Verified numbers its larger 480B sibling and the dense Qwen3.6-27B do, so you should benchmark it on your own codebase before betting a workflow on it. For the broad middle of developers who want one self-hostable, commercially-clean, agentic-capable coding model, it is the best all-around pick of 2026.

Best for

Developers who want one self-hostable, commercially-clean, agentic-capable coding model that fits a single 24GB GPU

Pricing

Free (open weights, Apache 2.0)

Source: Qwen3-Coder-30B-A3B-Instruct model card · Visit Qwen3-Coder-30B-A3B

Qwen3.6-27B is the model to pick when you want the strongest benchmarked coding performance you can run locally and you have a single large GPU to feed it. Unlike the MoE coder above, this is a dense 27-billion-parameter model, which means every parameter is active on every token — more demanding to run, but it posts the highest open-weight coding scores on this list. Its Apache-2.0 model card reports 77.2 on SWE-bench Verified, 53.5 on the harder SWE-bench Pro, 83.9 on LiveCodeBench v6 and 59.3 on Terminal-Bench 2.0, putting it within striking distance of closed frontier models on agentic software-engineering tasks at zero API cost and with full data control. It carries a 262,144-token native context that extends past a million tokens, and it released in April 2026 under Apache 2.0, so commercial use is unrestricted. The catch is hardware: a dense 27B model in a usable quantization wants roughly 18-24GB of VRAM and runs slower per token than the sparse MoE models, so on a modest laptop GPU you will feel the throughput cost. It is also a general-purpose model with strong coding rather than a fill-in-the-middle autocomplete specialist, so for pure inline Tab completion a smaller FIM model is snappier. If your priority is the best benchmarked local coding quality and you can supply the GPU, Qwen3.6-27B is the performance leader of 2026's open-weight field.

Best for

Developers who want the best benchmarked local coding quality and can dedicate a single large (~24GB) GPU to it

Pricing

Free (open weights, Apache 2.0)

Source: Qwen3.6-27B model card · Visit Qwen3.6-27B

Best value

DeepSeek-Coder-V2-Lite is the answer for developers whose constraint is the graphics card they already own. It is a mixture-of-experts model with 16 billion total parameters but only 2.4 billion active per token, and that sparsity is what lets it deliver genuinely useful code generation on a consumer GPU with roughly 10-12GB of VRAM, where the dense 27B models simply will not fit comfortably. Its model card documents a 128K context window and notes that it was further pre-trained from a DeepSeek-V2 checkpoint on six trillion tokens, expanding language coverage from 86 to 338 programming languages — broad enough that it rarely stumbles on whatever stack you throw at it. It remains a well-rounded chat-and-generate coding model: ask it to write a function, explain a stack trace or scaffold a module and it holds its own against much larger models for everyday work. The honest tradeoffs are real. It is a 2024-era release, so it trails the 2026 Qwen and DeepSeek-V3.2 generations on the hardest agentic SWE-bench tasks, and its license is the DeepSeek Model License rather than a pure Apache/MIT grant, so read the terms before shipping it commercially even though they do permit commercial use. For a developer who wants legitimate local coding help without buying a new GPU, the Lite model is the best-value entry point in 2026.

Best for

Developers on consumer GPUs (10-12GB VRAM) who want solid everyday code generation without buying new hardware

Pricing

Free (open weights, DeepSeek Model License)

Source: DeepSeek-Coder-V2-Lite-Instruct model card · Visit DeepSeek-Coder-V2-Lite

Codestral is Mistral AI's dedicated coding model, and it earns its place by being the best at the one thing developers feel most often: inline autocomplete. It is a 22-billion-parameter model trained on more than 80 programming languages and optimized for fill-in-the-middle (FIM) completion, meaning it reasons about the code both before and after your cursor rather than only what precedes it — which is why its completions slot naturally into existing functions. Mistral's Codestral 25.01 release added a more efficient architecture, a better tokenizer, a 256K context window and roughly 2x faster generation, positioning it as the strongest FIM model in its weight class; the 25.08 update reported further gains, including about a 30% increase in accepted completions and 50% fewer runaway generations. It is built for low-latency, high-frequency use, which is exactly the profile you want behind a Tab key. The important nuance is licensing: the original Codestral shipped under the restrictive Mistral Non-Production License, blocking commercial in-product use without a separate agreement — but Mistral relicensed the open-weight Codestral 2 (April 2026) under Apache 2.0, removing that barrier for shipping it in commercial IDEs and tooling. Verify which checkpoint and license you are pulling, since older weights still carry the restriction. For fast, accurate inline completion on a single 16GB GPU, Codestral is the autocomplete pick of 2026.

Best for

Developers who want the fastest, most accurate local inline autocomplete in VS Code or JetBrains on a single 16GB GPU

Pricing

Free open weights (Codestral 2 is Apache 2.0)

Source: Mistral AI — Codestral 25.01 · Visit Codestral

Devstral Small is Mistral's agent-focused coding model, purpose-built for the kind of multi-step software-engineering work that has come to define AI coding in 2026: not generating an isolated snippet, but operating inside a codebase to resolve a real issue across multiple files. Finetuned from Mistral-Small-3.1, it is a 24-billion-parameter dense model released under Apache 2.0, and its model card reports 53.6% on SWE-bench Verified using the OpenHands scaffold — a strong agentic result for a model this size and notably the kind of score that used to require a cloud frontier model. The headline practicality is that it runs on a single RTX 4090 or a Mac with 32GB of RAM, with a 128K context window, so a solo developer or a small team can stand up a genuinely agentic local coding setup without a server rack. Pairing it with an open-source agent harness lets it read issues, edit files and run tests in a loop, entirely offline. The honest weaknesses: as an agentic model it shines inside a scaffold like OpenHands but is less suited to raw inline autocomplete than Codestral, and its 53.6% SWE-bench Verified, while excellent for the size, still trails the largest dense and MoE models on the hardest tasks. For local, offline agentic coding on hardware you can actually afford, Devstral Small is the standout of 2026.

Best for

Solo developers and small teams who want offline agentic coding (read issue, edit, test, iterate) on a single RTX 4090 or 32GB Mac

Pricing

Free (open weights, Apache 2.0)

Source: Devstral Small model card · Visit Devstral Small

DeepSeek-V3.2 is the model for the team that wants frontier-class coding quality without sending code to a cloud API and is willing to provision real infrastructure to get it. It is a 685-billion-parameter mixture-of-experts model released under a permissive MIT license, which is unusually generous for a model at this capability tier — you can deploy it on internal infrastructure, fine-tune it on proprietary code and distill it without royalties or revenue share. On coding, its reported numbers are strong: SWE-bench Verified at 70 puts it among the top open-weight models on real multi-file GitHub-issue resolution, a touch behind the 77.2 leaders but with far more raw capacity and long-context headroom, and its architecture introduces DeepSeek Sparse Attention to keep long-context inference closer to linear cost. There is also a deep-reasoning DeepSeek-V3.2-Speciale variant, though note that variant is built for reasoning and does not support tool-calling, which matters for agentic coding workflows. The unavoidable weakness is scale: at 685B total parameters this is not a single-consumer-GPU model — it needs a multi-GPU server or a serious workstation to self-host, putting it out of reach for the laptop-class setups the rest of this list targets. If you are a regulated or privacy-sensitive organization that needs cloud-frontier coding quality on infrastructure you control, DeepSeek-V3.2 is the open-weight ceiling in 2026; for everyone else, the smaller models above are the practical local choice.

Best for

Regulated or privacy-sensitive teams that need cloud-frontier coding quality on infrastructure they fully control and can run a multi-GPU deployment

Pricing

Free (open weights, MIT license)

Source: DeepSeek-V3.2 model card · Visit DeepSeek-V3.2