What is the best local LLM for coding in 2026?

Qwen3 Coder 14B at Q4_K_M for 12-24 GB VRAM cards - the best quality-per-gigabyte in the coding category. Qwen3 Coder 32B at Q4 for 24 GB cards. DeepSeek Coder V3 (32B or full) if your hardware supports it and you need strongest raw completion quality.

Can I run a coding LLM on an 8 GB GPU?

Yes. Qwen3 Coder 7B at Q4_K_M fits in 6-7 GB and gives usable code completion quality. Gemma 3 4B-IT is another option if you also want a general assistant.

Which model is best for fill-in-middle (FIM) autocomplete?

Codestral 2 22B for pure FIM quality - it was trained with FIM as a first-class objective. Qwen3 Coder 14B is close behind and fits in a wider range of hardware.

What IDE extension should I use with a local coding LLM?

Continue.dev is the easiest choice for VS Code and JetBrains - open-source, Ollama-native, handles both chat and tab autocomplete. Cline is a good alternative for agent-style tasks that modify files autonomously.

How much VRAM do I need for a good coding LLM?

8 GB: 7B at Q4. 12 GB: 14B at Q4 (the sweet spot for quality vs resource). 24 GB: 32B at Q4 - noticeably smarter for complex multi-file tasks. 48 GB+: full DeepSeek Coder V3 or Qwen3 Coder 70B.

Is DeepSeek Coder V3 better than Qwen3 Coder?

On raw HumanEval and SWE-bench benchmarks, DeepSeek Coder V3 and Qwen3 Coder 72B trade wins. For local use on consumer hardware, Qwen3 Coder 14B or 32B is usually more practical because it fits in a wider range of VRAM configurations.

Should I use a coding-specific model or a general model for code?

Coding-specific models (Qwen Coder, DeepSeek Coder, Codestral) win on fill-in-middle completion and repository-level reasoning. General models like Qwen3 14B-Instruct can be close behind on instruction-following tasks. For dedicated coding workflows, the specialized models are worth the extra few GB of context they need.

Best Local LLM for Coding in 2026: Qwen Coder, DeepSeek Coder V3 & More

By the RunLocalModel editorial team · Published May 13, 2026 · ~13 minute read

If you only read one paragraph For most hardware tiers in 2026, Qwen3 Coder 14B at Q4_K_M is the default pick: it fits in 12 GB, it beats every coding model its size on HumanEval and SWE-bench variants, and it handles fill-in-middle (FIM) autocomplete well. Step up to Qwen3 Coder 32B if you have 24 GB. If your workflow is pure autocomplete (not chat), Codestral 2 22B still produces the cleanest FIM completions on its training languages. For agent-level tasks that span whole repositories, DeepSeek Coder V3 is the strongest open model — if your hardware can hold it. Use Continue.dev on VS Code or Cline for agent tasks; both are one-click Ollama-native.

Quick answers

Best local coding LLM in 2026 overall?: Qwen3 Coder 14B at Q4_K_M for 12–24 GB cards. Qwen3 Coder 32B at Q4 for 24 GB. DeepSeek Coder V3 if you can hold its full size.
Can I run a coding LLM on an 8 GB GPU?: Yes. Qwen3 Coder 7B at Q4_K_M fits in 6–7 GB and gives usable autocomplete quality.
Best model for FIM autocomplete?: Codestral 2 22B for pure FIM quality. Qwen3 Coder 14B is close behind and fits more hardware.
Best IDE extension for local coding LLMs?: Continue.dev for VS Code / JetBrains — open-source, Ollama-native, handles both chat and tab autocomplete.
Is DeepSeek Coder V3 better than Qwen3 Coder?: On benchmarks they trade wins. For local use on consumer hardware, Qwen3 Coder 14B or 32B is more practical because it fits in a wider range of VRAM.
How much VRAM for a good coding LLM?: 8 GB: 7B at Q4. 12 GB: 14B at Q4 (sweet spot). 24 GB: 32B at Q4. 48 GB+: full DeepSeek Coder V3.

The models, one by one

Qwen3 Coder (7B / 14B / 32B / 72B)

Qwen3 Coder from Alibaba is the strongest overall coding model family available locally in 2026. It handles instruction following, fill-in-middle, multi-language code generation, and repository-level reasoning well at every size tier. The 14B variant is the community default — best quality-per-gigabyte in the coding category. 32B takes a visible step up on complex, multi-file tasks (it is noticeably better at maintaining cross-file context). 72B approaches frontier quality and needs 48+ GB.

Context window is long (up to 128k tokens on the instruct versions), which matters for whole-file editing and code review. Pull the instruct variant for chat; the base variant for FIM in llama.cpp server mode.

DeepSeek Coder V3 (21B MoE / 236B full)

DeepSeek Coder V3 is, on raw benchmarks, the strongest open coding model in 2026 — it competes directly with Claude 3.5 Sonnet and GPT-4o on HumanEval, SWE-bench, and LiveCodeBench. The catch: the full model is a 236B MoE architecture. At Q4_K_M, the active parameters per forward pass are ~21B, so it runs at ~21B speed — but the total weights on disk require 100+ GB of memory to hold. This means a 128 GB Mac Studio Ultra, a dual-workstation setup, or just using the 236B at very aggressive quant on a server. For most home users, this model is a hosted-API story, not a local one. There is a smaller DeepSeek-Coder-V2-Lite (16B) that fits more hardware; it is still excellent but cannot match the full V3 on hard tasks.

Codestral 2 22B (Mistral)

Codestral 2 from Mistral AI is the specialist fill-in-middle model of the generation. Mistral trained it with FIM as a first-class objective across Python, JavaScript/TypeScript, Rust, Go, Java, C/C++, and SQL. On autocomplete benchmarks, it consistently produces cleaner completions than same-size instruction-tuned generalists, and its 22B size makes it comfortable on a 24 GB card (under 14 GB at Q4_K_M). The trade-off: it is somewhat weaker than Qwen3 Coder 14B on instruction-style tasks like "explain this codebase" or "write a FastAPI endpoint from scratch." Use Codestral if your workflow is 90%+ tab completion; use Qwen3 Coder if you also want a chat companion.

Gemma 3 12B (Google)

Gemma 3 12B is not a coding-specialist model, but it is worth listing here because: (a) it fits in 10 GB at Q4_K_M, (b) its instruction-following quality is very strong for its size, and (c) if you already have a Gemma 3 setup from our phone guide you can use the same model family on your desktop with a bigger parameter count. On coding tasks it is competitive with Qwen3 Coder 7B but trails 14B on complex tasks. Best for: lighter hardware where a specialist coding model would not fit and you want one model for everything.

Head-to-head benchmark table

Representative scores from public evaluations (HumanEval pass@1, MultiPL-E Python, SWE-bench Verified where available). Numbers are approximate consensus from the leaderboards at the time of writing.

Model	Size (params)	VRAM at Q4_K_M	HumanEval ~pass@1	SWE-bench Verified	FIM quality
Qwen3 Coder 7B	7B	~5.5 GB	~72%	—	Good
Qwen3 Coder 14B	14B	~10 GB	~82%	~20%	Very good
Codestral 2 22B	22B	~13.5 GB	~78%	—	Excellent
Qwen3 Coder 32B	32B	~20 GB	~87%	~25%	Excellent
DeepSeek Coder V2 Lite	16B active	~10 GB	~81%	~18%	Very good
DeepSeek Coder V3 (full)	21B active / 236B total	100+ GB total	~91%	~42%	Excellent
Gemma 3 12B	12B	~8.5 GB	~70%	—	Adequate

HumanEval and SWE-bench scores vary by prompt template, sampling settings, and evaluator version. Treat these as relative comparisons, not absolute benchmarks. The EvalPlus leaderboard and SWE-bench leaderboard are the most up-to-date public references.

Recommended pick by hardware tier

Your VRAM	Recommended model	Quant	Notes
8 GB	Qwen3 Coder 7B	Q4_K_M	Tight fit; leave headroom with q8_0 KV cache.
12 GB	Qwen3 Coder 14B	Q4_K_M	Sweet spot. Also fits Codestral 2 22B at IQ3_XXS.
16 GB	Qwen3 Coder 14B	Q5_K_M	Step up from 12 GB; noticeably better at math-heavy code.
24 GB	Qwen3 Coder 32B	Q4_K_M	The biggest coding model that fits on a single consumer card.
24 GB (FIM focus)	Codestral 2 22B	Q5_K_M	Best-in-class FIM completions, comfortable at 24 GB.
48 GB+	Qwen3 Coder 72B or DSCoder V3 Lite	Q4_K_M	Near-frontier quality; comfortable at long context.

Connecting to your IDE

Option A (recommended): Continue.dev on VS Code

Continue.dev is the most mature open-source IDE extension for local LLMs. It runs on VS Code and JetBrains IDEs, talks to Ollama (and LM Studio's local server) out of the box, and handles both chat and fill-in-middle tab autocomplete.

1. Pull the model: ollama pull qwen2.5-coder:14b-instruct-q4_K_M

2. Install Continue: open VS Code Extensions (Ctrl+Shift+X), search "Continue", install the open-source extension. No account required.

3. Add the Ollama provider: click the Continue sidebar, open the model selector, choose "Add Model", select "Ollama", then pick the model you pulled.

4. Enable tab completion: open Continue settings (gear icon), toggle "Tab Autocomplete" ON. Continue uses the same local model for FIM as you type.

5. Test it: open any code file, start typing a function, pause. A grey completion appears; Tab to accept.

Separate FIM model tip: For the fastest tab completions, point the "Autocomplete model" in Continue settings to a smaller, faster model (e.g. qwen2.5-coder:7b-base base variant) and keep the 14B for chat. Two models, two purposes.

Option B: Cline (VS Code) for agent tasks

Cline is an autonomous coding agent that can read your whole repository, write files, run terminal commands, and iterate on bugs across multiple files — locally, using an Ollama or LM Studio backend. It exposes an OpenAI-compatible endpoint, so you point it at http://localhost:11434/v1. Cline works best with larger models (32B+) that can hold a coherent plan across many tool calls. For multi-file agent tasks, Qwen3 Coder 32B + Cline is the best consumer-hardware local agent stack in 2026.

Option C: LM Studio local server + any chat client

Enable the LM Studio local server (it exposes an OpenAI-compatible API at http://localhost:1234/v1). Then configure your editor’s AI extension to use that endpoint. This approach works with any extension that lets you set a custom API base URL: Continue, GitHub Copilot Chat (unofficial forks), Cursor (local mode), and others.

What context length actually matters for coding

Coding is the use case where context length matters most. A short autocomplete needs only 2–4k tokens. But explaining a bug in a 1,000-line file, reviewing a PR with diffs, or running Cline on a medium-size repository can easily require 32k–64k tokens of context. Models that advertise 128k context windows are limited in practice by how much fits in your VRAM at the quantization you chose.

A 14B model at Q4_K_M with a 32k context and q8_0 KV cache uses roughly 14 GB total — right at the 16 GB VRAM ceiling. If you need longer context, either: (a) reduce the context window, (b) switch to q4_0 KV cache (quality cost on long contexts — see our quantization guide), or (c) use a smaller model. Use our home-page checker to estimate exact memory for your model + context combination.

What is changing in 2026

Two trends matter. First, the SWE-bench vertical is compressing — models trained specifically on issue-resolution tasks are now giving higher SWE-bench scores at smaller sizes. Qwen3 Coder and DeepSeek Coder V3 both benefit from this; expect even more specialization in the next generation. Second, IDE extensions are catching up: Continue.dev’s FIM quality and Cline’s agent loops both improved significantly in 2025–2026, making the local coding experience far more competitive with hosted products like GitHub Copilot.

For now, the recommendation is clear: if you are writing code regularly and have 12+ GB of VRAM, a local Qwen3 Coder 14B in Continue.dev will handle most daily tasks without sending a single keystroke to the cloud.