Somewhere around the fourth Copilot price hike, a lot of developers had the same thought at the same time: why am I paying a subscription to autocomplete my own code?
Fair question. And honestly, the answer used to be “because open models weren’t good enough.” That excuse expired sometime in 2026. The best open source LLM for coding right now isn’t a scrappy runner-up anymore — a few of them are genuinely trading blows with GPT and Gemini-class systems on real engineering benchmarks, and you can run some of them on a single GPU sitting under your desk.
This is the guide I wish existed when I started digging through benchmark spreadsheets at 1am trying to figure out which model actually deserved my 4090’s VRAM. Real scores, real hardware numbers, real Ollama commands. No vibes-based rankings.
The quick answer
If you just want a name before you keep reading: Kimi K3 currently leads the open pack on real-world coding benchmarks, with DeepSeek-V4 and Qwen3.6 right behind it depending on your hardware budget. If you’ve got 24GB of VRAM and want something that actually fits, Qwen3-Coder-30B-A3B is the one to pull first.
Now let’s get into why.
The 8 models actually worth your disk space
1. Kimi K3 (Moonshot AI) — This is the one that made “open-weight” and “frontier” stop being contradictory terms. It’s a 2.8-trillion-parameter mixture-of-experts model, but only about 104B parameters activate per token, which is the whole trick behind why it’s usable at all. It tops Terminal-Bench and holds its own against Gemini 3.1 Pro on GPQA. You’re not running this at home unless “home” is a rack of A100s, but it’s the ceiling right now.
2. DeepSeek-V4-Pro — DeepSeek keeps doing the thing where they release something under a genuinely permissive MIT license and everyone else has to scramble. V4-Pro hits over 80% on SWE-bench Verified, has a million-token context window, and — this is the part people skip — comes in a Flash variant that’s dramatically cheaper to run while barely giving up performance. If you want frontier-adjacent reasoning without a custom license nobody read, start here.
3. Qwen3.6-27B — The single-GPU champion. Dense, not MoE, which means the VRAM math is predictable, and it still clears 77% on SWE-bench Verified. This is the model most people mean when they type “best open source llm for coding” into Google and actually want something they can run tonight.
4. Qwen3-Coder-30B-A3B — Technically a step down in raw benchmark score from the 27B dense model, but the MoE architecture means it feels faster than its size suggests — 3B active parameters doing the heavy lifting. It’s become the default pick for local agentic coding setups because it’s the sweet spot between “fits on one GPU” and “doesn’t feel like talking to a smaller model.”
5. GLM-5.2 (Z.ai) — If licensing is the thing keeping your legal team up at night, this is your answer. Plain MIT, no asterisks, and it still lands over 60% on SWE-bench Pro and 81 on Terminal-Bench 2.1. Not the top scorer, but the least complicated to actually ship in a commercial product.
6. gpt-oss-120b (OpenAI) — Yes, that OpenAI. Their first real open-weight release since GPT-2, and it’s specifically strong at competitive programming — over 2600 Codeforces Elo. Apache 2.0 licensed, which still catches people off guard given who made it.
7. Devstral — Purpose-built for agentic software engineering rather than raw benchmark chasing. It’s not going to top a leaderboard, but if what you actually want is a model that plans, edits multiple files, and doesn’t lose the thread halfway through a refactor, it punches above its size.
8. Llama 4 Scout — Meta’s contribution here is context length, not coding score. A 10-million-token window is absurd on paper, and it does let you throw entire codebases at it in one go. Just know the coding benchmarks trail the others on this list — you’re paying for context, not correctness.
The benchmark chart everyone actually wants to see
Quick honesty check before the numbers: HumanEval is basically a solved test at this point. Every serious model clears 85-90%+ on it, which makes it nearly useless for telling models apart — it’s a bit like judging marathon runners by whether they can walk up a flight of stairs. SWE-bench Verified (real GitHub issues, real repos) and Terminal-Bench (actual shell/agent tasks) are what separates the models that write plausible code from the ones that fix real bugs.
| Model | HumanEval | SWE-bench Verified | Terminal-Bench 2.1 | Params (active) |
|---|---|---|---|---|
| Kimi K3 | ~94% | 81.2% | 88.3 | 2.8T (104B) |
| DeepSeek-V4-Pro | ~92% | 80.6% | — | 1.6T (49B) |
| Qwen3.6-27B | ~91% | 77.2% | — | 27B (dense) |
| Qwen3-Coder-30B-A3B | ~89% | 73.4% | — | 30B (3B) |
| GLM-5.2 | ~90% | 62.1%* | 81.0 | 753B (40B) |
| gpt-oss-120b | ~89% | — | — | 117B (5.1B) |
| Devstral | ~87% | ~68% | — | ~24B |
| Llama 4 Scout | ~82% | 68.0% | — | 109B (17B) |
*GLM-5.2’s SWE-bench figure is on the harder “Pro” variant of the benchmark, not the standard Verified split — so it’s not a like-for-like comparison with the row above it, just the most credible number available.
“Llama 3 vs DeepSeek Coder vs CodeLlama” — why that question is out of date
If you’re still comparing Llama 3, DeepSeek Coder, and CodeLlama, I get why — those three basically defined the “open coding model” conversation back in 2024. But none of them are the right answer anymore, and it’s worth being straight about that instead of pretending the comparison still matters.
CodeLlama hasn’t been updated in ages and gets outclassed by models a fraction of its size now. The original DeepSeek Coder was folded into the broader DeepSeek-V line, and V4 makes the old Coder models look like a rough draft. Llama 3 itself has been superseded twice over by Llama 4’s Scout and Maverick variants — which, to be fair, still lag behind Qwen and DeepSeek on pure coding benchmarks, context window aside.
So if that’s the search that brought you here: swap DeepSeek Coder for DeepSeek-V4-Flash, swap CodeLlama for Qwen3-Coder, and swap Llama 3 for either Llama 4 Scout (context) or just skip Meta entirely and grab Qwen3.6 (coding score). Same instinct, better answer.
Hardware you actually need
This is where most guides get vague, so let’s not. VRAM requirements below assume Q4_K_M quantization, which is the size/quality sweet spot almost everyone should run.
| VRAM / RAM | What fits | Real-world example |
|---|---|---|
| 8GB | 7-8B dense models | Llama 3.1 8B, quick autocomplete |
| 16GB | ~20B models | Mistral Small (Q4), light agentic work |
| 24GB | 30-32B, incl. MoE | Qwen3-Coder-30B-A3B, Qwen3.6-27B — the sweet spot |
| 32GB | ~45B with headroom | Qwen3.6-27B + long context |
| 48-64GB | 70B dense | Llama 3.1 70B, workstation territory |
| 128GB+ (unified/Mac) | 120-235B MoE | Qwen3 235B, partial DeepSeek loads |
| 256GB+ / multi-GPU | 480B+ MoE | Full Qwen3-Coder-480B, Kimi K3 |
One thing that trips people up: MoE models like Qwen3-Coder-30B-A3B need enough memory to hold all 30B parameters even though only 3B activate per token. The “A3B” in the name is about speed, not storage — don’t undersize your RAM thinking it’s a 3B model.
Running a coding LLM locally with Ollama
Here’s the part that actually gets you typing instead of reading. Assuming you’ve got a 24GB card:
- Install Ollama — one line, works on Mac, Linux, and Windows:
curl -fsSL https://ollama.com/install.sh | sh
- Pull the model. For Qwen3-Coder at Q4_K_M:
ollama pull qwen3-coder:30b
- Run it directly in the terminal to sanity-check it loads:
ollama run qwen3-coder:30b
- Wire it into your editor. Ollama exposes an OpenAI-compatible endpoint at
http://localhost:11434/v1— point Cline, Continue.dev, or Aider at that URL instead of OpenAI’s, and you’ve got in-editor completions with nothing leaving your machine. - Watch your context window. Full context on a 30B model can add over 10GB on top of the base weights. If you’re hitting out-of-memory errors, trim
num_ctxdown before you assume the model’s too big.
If your GPU’s smaller than that, drop to Qwen3.6-14B or Gemma 4 12B — the quality gap is smaller than the VRAM gap.
Best coding AI agent (not just autocomplete)
A raw model is only half the story — the “best coding ai agent” search is really asking about the layer on top: something that plans, edits several files, runs tests, and loops until the bug’s actually gone. Devstral was built with exactly this in mind, and it pairs well with Cline or OpenClaw as the driving agent. Qwen3-Coder and DeepSeek-V4 also handle tool-calling reliably enough for multi-step agent loops, which matters more than raw HumanEval score once you’re asking a model to actually operate inside a repo unsupervised.
The honest Copilot-alternative verdict
If you want something that just works today with zero setup, Copilot’s still going to feel smoother out of the box — that’s the trade you’re making. But if what you actually want is no rate limits, no per-seat billing, and code that never leaves your network, Qwen3-Coder-30B-A3B through Ollama plus Cline is, right now, the closest open setup gets to that Copilot feeling. GLM-5.2 is worth a look too if your company’s legal team specifically vetoes anything without a plain MIT license — that objection kills more open-source adoption than people admit.
There’s also a cost angle nobody talks about enough. A Copilot Business seat runs you a monthly fee whether you write ten lines or ten thousand that day. A local model costs you electricity and the GPU you probably already own for something else. If you’re a solo developer or a small team, that math adds up fast over a year — and it’s the reason “github copilot alternatives” has quietly become one of the most searched phrases in dev communities this year.
Quick answers to what people actually ask
What’s the single best open source LLM for coding right now? Kimi K3 tops the raw benchmarks, but Qwen3.6-27B is the realistic best pick if you’re running it yourself rather than renting a GPU cluster.
Can I run a good coding LLM on 8GB VRAM? Yes, with real limits. You’re looking at 7-8B dense models like Llama 3.1 8B — fine for autocomplete and small functions, not for agentic multi-file refactors.
Is DeepSeek still worth using in 2026? Very much so. DeepSeek-V4-Pro and V4-Flash remain among the strongest MIT-licensed options for anyone who wants frontier-adjacent reasoning without paying per token.
Do I need Ollama specifically, or does LM Studio work too? Either works. Ollama’s the more common choice for editor integrations because of its OpenAI-compatible API; LM Studio’s GUI is friendlier if you’d rather not touch a terminal at all.
Will a local model actually replace Copilot for me? For a solo developer or small team willing to spend one evening on setup, honestly — yes, close enough that the gap is more about polish than capability. For a large team standardizing tooling across dozens of engineers, the calculus shifts back toward paying for the managed version.
Give it an evening. Worst case, you’ve learned your hardware’s real limits. Best case, you’ve quietly cancelled a subscription.
