If you searched "open source LLM leaderboard" hoping for one ranked list, here's the honest answer: there are two lists, and a founder needs the second one. The models topping the open-weight boards in September 2026 — Kimi K3, DeepSeek V4 Pro, Qwen3-Max-class, GLM-5.3 — are trillion-parameter mixture-of-experts giants that only run in a datacenter. The models you can actually download and run on one 24GB GPU are a shorter, different list led by gpt-oss-20b and the Qwen3 dense family, both Apache-2.0. And the filter that decides which you can put in a product isn't the benchmark at all — it's the license.
So before the table, three things most leaderboard pages won't tell you:
- "Tops the board" and "runs on your machine" are different questions. The best open model and the best local open model are rarely the same file.
- License is the real ranking for anyone shipping. Apache-2.0 and MIT you can build on freely; Llama, Gemma, and Kimi K3's licenses carry conditions you must read first.
- The Hugging Face Open LLM Leaderboard is retired (archived in 2025). If you're reading rankings there, they're stale — the live references are Artificial Analysis and Arena (formerly LMArena).
The leaderboard, split the way a founder should read it#
Here is the open-weight field in September 2026, ordered by reported general capability but annotated with the two facts that actually decide your build — what it's licensed for, and what it takes to run:
- Kimi K3 (Moonshot) — cited among the strongest open models for frontier reasoning, long context, and multimodal work. But it ships under a custom revenue-tiered license (free for small use; a paid agreement and attribution kick in above set revenue/MAU thresholds), and at ~2T-plus parameters it's datacenter-only. A model to call via API, not own.
- DeepSeek V4 Pro — top-tier open reasoning and coding, near the closed frontier on agentic tasks, under a clean MIT license. Still a ~1.6T MoE, so datacenter-only in practice; its smaller V4 Flash sibling (including the Sept 10 V4.1 Flash) is the runnable tier for most teams.
- Qwen3-Max-class (Alibaba) — a leading open general-and-agentic model on several boards. Note the license split: the Max-class weights ship under a custom license, while the smaller Qwen3 dense models are Apache-2.0. Datacenter-scale to run.
- GLM-5.3 / GLM-5.2 (Z.ai) — tops several open coding boards and is strong general-purpose. License varies by checkpoint: GLM-5.2 is MIT, GLM-5.3 shipped under a custom license, and GLM-5.3-Flash is MIT — verify the exact weight you download. Multi-GPU to run; the Flash variant is more tractable.
- Llama 4 Scout / Maverick (Meta) — no longer at the top of the boards, but the deepest tooling ecosystem and, in Scout, an enormous context window. The Llama 4 Community License is free under 700M monthly active users but carries use restrictions and isn't OSI-open. Scout runs on a big single GPU or workstation when quantized; Maverick is multi-GPU.
- Mistral Large 3 — the clean-license European flagship, Apache-2.0, competitive if generally a step below the top Chinese-lab open models. Datacenter/multi-GPU MoE.
- gpt-oss-120b / 20b (OpenAI) — the most practical openly-licensed models here, Apache-2.0. gpt-oss-120b runs on a single 80GB GPU; gpt-oss-20b is the headline for local use — it fits in ~12–16GB of VRAM, so even a 16GB card runs it.
- Qwen3 dense (4B–32B) — the best generalists you can fully own, all Apache-2.0. The 32B fits a 24GB card at 4-bit; the 8B and 14B run comfortably on modest hardware.
The pattern is the story: capability climbs with parameter count, but shippability and runnability fall. The four models at the top of the board are the four you're least likely to run yourself.
The list that matters if you're running it locally#
This is the answer to "open source LLM models to run locally." Every figure below is approximate, at Q4-class quantization, for a single 24GB consumer GPU (RTX 3090/4090 class) unless noted:
| Model | Type / size | Approx. VRAM (4-bit) | License | Best for |
|---|---|---|---|---|
| gpt-oss-20b | MoE, ~21B (3.6B active) | ~12–16GB | Apache-2.0 | Fast local reasoning + agent loops; fits 16GB too |
| Qwen3-32B | Dense 32B | ~20GB (tight) | Apache-2.0 | Strongest dense generalist on one 24GB card |
| Qwen3-30B-A3B | MoE 30B (3B active) | ~20–24GB | Apache-2.0 | Fast MoE agent loops (all experts must load) |
| Gemma 3 27B | Dense 27B, multimodal | ~15–16GB | Gemma license (not OSI) | Strong multimodal generalist, locally |
| DeepSeek-R1-Distill-Qwen-32B | Dense 32B distill | ~18–20GB | MIT | Reasoning on a single card |
| Qwen3-14B | Dense 14B | ~12–16GB | Apache-2.0 | Balanced everyday generalist |
| Phi-4 / Phi-4-Reasoning | Dense ~14B | ~8–12GB | MIT | Reasoning on modest hardware |
| Gemma 3 12B | Dense 12B, multimodal | ~7GB | Gemma license (not OSI) | Efficient multimodal, laptop-friendly |
| Qwen3-8B / 4B | Dense 8B / 4B | ~8GB / ~4–8GB | Apache-2.0 | Laptop-class chat that punches up |
Two practical notes. Quantization: Q4_K_M is the standard trade — roughly a 75% size cut for little quality loss — but move to Q5 or Q8 if you have the VRAM, especially for reasoning-heavy work where low-bit quantization bites hardest. MoE VRAM: a mixture-of-experts model like Qwen3-30B-A3B only activates 3B parameters per token, but you still have to load all 30B into memory, so budget for the total, not the active count.
The license table you should read before the benchmark#
For a founder, this is the ranking that decides your business, so it's worth stating plainly which licenses are clean and which have strings — as of this writing, and always confirm on the model card, because terms change per checkpoint:
- Ship it freely (Apache-2.0): Qwen3 dense family, gpt-oss-120b/20b, Mistral Large 3.
- Ship it freely (MIT): DeepSeek V4, Phi-4, GLM-5.2.
- Read the conditions first: Llama 4 (Community License — free under 700M MAU, with use restrictions), Gemma 3 (commercial-friendly but not OSI-open), Kimi K3 (revenue-tiered — paid agreement and attribution above thresholds), GLM-5.3 (custom license, unlike 5.2), Qwen3-Max-class (custom license, unlike the Apache-2.0 dense line).
"Open weights" means you can download the file. It does not automatically mean you can build a product on it — the Kimi K3 and Llama distinctions are exactly where teams get surprised.
So which one should you actually pick?#
- You want the smartest open model and you'll call it over an API: Kimi K3, DeepSeek V4 Pro, or Qwen3-Max-class — but price a hosted API against owning the hardware, because unless you keep rented GPUs busy around the clock, the API almost always wins. The math is the same one in our GPU rental price map and LLM API pricing breakdown.
- You want to run it yourself on one GPU and ship it in a product: gpt-oss-20b or a Qwen3 dense model (Apache-2.0). Clean license, single-card VRAM, real capability.
- You specifically want the best open coder: that's a different bake-off with its own hardware math — we ranked it in Open-Source LLMs for Coding, September 2026.
- You'd rather not own any hardware but still self-host: run one of the mid models on a scale-to-zero serverless GPU and pay only for the seconds it's warm.
The leaderboard is a useful place to start and a terrible place to stop. The model at the top of Artificial Analysis this month may be a trillion parameters you'll never run and a license you'll have to negotiate. The model that quietly wins your build is the one that fits your GPU, carries a license you can ship, and is good enough for the 90% of work that doesn't need the frontier. For most founders in September 2026, that model is Apache-2.0 and fits in 24GB.
