# Best Local LLMs for Coding in 2026

> We ranked the open-weight coding models you can actually run on your own hardware in 2026 — with real SWE-bench scores, VRAM requirements, licenses and the honest tradeoffs.

*Published 2026-06-14 · Updated 2026-06-14 · By Marcus Vance*

Coding is the workload where local LLMs have closed the gap with the cloud fastest. In 2026 you can run an open-weight coding model on a single GPU and get completions, refactors and agentic bug-fixes that needed a frontier API a year earlier — and your source code never leaves the machine. MoE architectures activate only a few billion parameters per token, so a 30B coding model fits on a 24GB consumer card. The on-device AI market was ~$10.2B in 2024 and is projected to reach $92.76B by 2033 (27.8% CAGR, SkyQuest).

**The ranking:**

1. **Qwen3-Coder-30B-A3B** — Apache 2.0, MoE (30.5B/3.3B active), 256K context. Best overall local coding model.
2. **Qwen3.6-27B** — Apache 2.0 dense 27B, 77.2 SWE-bench Verified. Highest open-weight benchmarks on one big GPU.
3. **DeepSeek-Coder-V2-Lite** — 16B/2.4B active, 128K, 338 languages. Best for tight VRAM (10-12GB).
4. **Codestral** — 22B, FIM-optimized, 256K. Best IDE autocomplete.
5. **Devstral Small** — 24B, Apache 2.0, 53.6% SWE-bench Verified. Best agentic model on a single RTX 4090.
6. **DeepSeek-V3.2** — 671B MoE, MIT, 77.2 SWE-bench Verified. Frontier-quality for teams with serious hardware.

Run them locally with Ollama plus an editor extension like Continue.dev. Independent ranking; every model carries an honest weakness; benchmarks cite official model cards. Last updated 2026-06-14.

## Sources

1. [Qwen3-Coder-30B-A3B-Instruct (model card: 30.5B total / 3.3B active, 256K context, Apache 2.0)](https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct)
2. [Qwen3.6-27B (model card: 77.2 SWE-bench Verified, 83.9 LiveCodeBench v6, Apache 2.0)](https://huggingface.co/Qwen/Qwen3.6-27B)
3. [DeepSeek-Coder-V2-Lite-Instruct (model card: 16B total / 2.4B active, 128K context, 338 languages)](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct)
4. [DeepSeek-V3.2 (model card: 685B MoE, MIT license, SWE-bench Verified 70, DeepSeek Sparse Attention)](https://huggingface.co/deepseek-ai/DeepSeek-V3.2)
5. [Codestral 25.01: 256K context, 2x faster, SOTA fill-in-the-middle](https://mistral.ai/news/codestral-2501/)
6. [Codestral 25.08 and the Mistral coding stack: on-prem/VPC deployment, FIM gains](https://mistral.ai/news/codestral-25-08/)
7. [Devstral Small (24B, Apache 2.0, 53.6% SWE-bench Verified, runs on a single RTX 4090)](https://huggingface.co/mistralai/Devstral-Small-2507)
8. [Ollama — open-source local LLM runtime (MIT licensed)](https://github.com/ollama/ollama)
9. [Continue — open-source, local-first AI code assistant for VS Code and JetBrains](https://github.com/continuedev/continue)
10. [On-Device AI Market: USD 10.2B (2024) to USD 92.76B (2033), 27.8% CAGR](https://www.skyquestt.com/report/on-device-ai-market)

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Source: https://aiintelreport.com/enterprise-ai/best-local-llms-for-coding-2026
Index: https://aiintelreport.com/llms.txt · Full text: https://aiintelreport.com/llms-full.txt