BenchLM recommendation
Best Ollama Models (July 2026): Ranked + Pull Commands
The best Ollama model for most machines is Qwen3.6-27B (public score 53.82): a ~16GB Q4_K_M pull that fits one 24GB GPU and posts a 77.2 SWE-bench Verified, making it the practical coding pick too. The highest-scoring model you can serve through Ollama is Kimi K2.6 (56.79), but its ~600GB Q4 weights need a multi-GPU server. On an 8GB card, run Mellum2-12B-A2.5B-Thinking (unranked).
Ollama is the easiest way to run a large language model on your own hardware: one ollama pull downloads a quantized GGUF build of a model, and ollama run serves it locally with an OpenAI-compatible API — on macOS, Windows, or Linux, with no CUDA setup or Python environment. By default the library ships models at Q4_K_M quantization, a 4-bit format that cuts weights to roughly 0.6GB per billion parameters with a modest quality cost.
That means only open-weight models qualify for this page — models whose weights are publicly downloadable. Proprietary APIs like GPT-5.4 or Claude can never run in Ollama. Every score below comes from the same public lane as the main leaderboard, so you can compare these picks directly against our open-source ranking and the hardware-tiered best local LLMs guide. Parameter counts come from BenchLM's self-host catalog or the official model names; Q4_K_M sizes are estimates from parameter count (~0.6GB per billion), not measured downloads.
The best Ollama models, ranked
Ten open-weight models worth pulling in July 2026, sorted strictly by BenchLM score. The top two are frontier MoE models that technically run through Ollama but realistically need datacenter hardware; everything from Qwen3.6-27B down fits a single consumer GPU or a Mac with enough unified memory.
| Model | BenchLM score | Params | Est. Q4_K_M size | Min hardware (Q4) | Pull command |
|---|---|---|---|---|---|
| GLM-5.1Z.AI | 67.74 | 744B MoE (40B active) | ~446GB | 8× NVIDIA H100 (80GB) | ollama pull glm |
| Gemma 4 31BGoogle | 61.08 | 31B dense | ~19GB | NVIDIA RTX 4090 (24GB) | ollama pull gemma4 |
| Kimi K2.5Moonshot AI | 59.66 | 120B dense | ~72GB | 4× NVIDIA A100 (80GB) | ollama pull kimi-k2 |
| Kimi K2.6Moonshot AI | 56.79 | 1000B MoE (32B active) | ~600GB | 8× NVIDIA H100 (80GB) | ollama pull kimi-k2 |
| Qwen3.6-27BAlibaba | 53.82 | 27B dense | ~16GB | NVIDIA RTX 4090 (24GB) | ollama pull qwen3.6 |
| DeepSeek-R1DeepSeek | 51.67 | 671B MoE (37B active) | ~403GB | 8× NVIDIA H100 (80GB) | ollama pull deepseek-r1:671b |
| Qwen3.6-35B-A3BAlibaba | 51.47 | 35B MoE (3B active) | ~21GB | 24GB GPU or 32GB Mac | ollama pull qwen3.6 |
| Gemma 4 12BGoogle | 47.29 | 12B dense | ~7GB | 8–12GB GPU or 16GB Mac | ollama pull gemma4 |
| Mistral Small 4Mistral | 46.23 | 119B MoE (22B active) | ~71GB | 2× NVIDIA RTX 5090 (32GB GDDR7) | ollama pull mistral-small |
Pull commands use each model's common Ollama library family name. Newer releases often add versioned or size-suffixed tags (like deepseek-r1:671b) — check ollama.com/library for the exact tag and available sizes before pulling. Q4_K_M sizes are estimates from parameter count.
Best Ollama model for coding
On a single 24GB GPU, Qwen3.6-27B is the best coding model you can pull: 77.2 on SWE-bench Verified and 83.9 on LiveCodeBench from a dense 27B whose Q4_K_M build is only ~16GB, with a 262K context for repository-scale work. If you have server-class hardware, Kimi K2.6is the outright coding leader among open weights — 80.2 SWE-bench Verified and 89.6 LiveCodeBench — but its 1T-parameter MoE demands roughly 600GB of memory at Q4. On an 8GB card, JetBrains' Mellum2-12B-A2.5B-Thinking (69.9 LiveCodeBench) is a purpose-built coding MoE with just 2.5B active parameters, so it stays fast even partially offloaded to CPU. Pair any of them with Ollama's local API and your editor's OpenAI-compatible endpoint setting.
Best small Ollama models (8–16GB)
With 8–16GB of VRAM or unified memory you are shopping in the 8–14B class, where Q4_K_M builds land around 5–8GB. Mellum2-12B-A2.5B-Thinking (unranked) is the standout: a ~7GB pull for modest hardware. Google's Gemma 4 12B(52) is the best general-purpose dense option at the same ~7GB size, and Zyphra's ZAYA1-8B (55) is the strongest pure 8B, though it is not in the Ollama library — import its GGUF from Hugging Face instead. For reasoning on small cards, the distilled deepseek-r1 tags ship sizes from 7B to 70B.
How to choose an Ollama model
- Start from your memory, not the leaderboard. At Q4_K_M, budget ~0.6GB per billion parameters for weights, then leave 20–30% headroom for context (KV cache). A 24GB card comfortably runs a ~16GB model; it will not run a ~21GB one at useful context lengths.
- Prefer MoE models when you must offload to CPU. Sparse models like Mellum2 (2.5B active) or Qwen3.6-35B-A3B (3B active) only compute a fraction of their weights per token, so they stay usable on Macs and mixed CPU/GPU setups where dense models crawl.
- Pick by task, not just overall score. For coding, weight SWE-bench Verified and LiveCodeBench; Qwen3.6-27B beats several models that outscore it overall.
- Check the license.Apache-2.0 (Qwen, Gemma's terms differ slightly) and MIT (GLM-5.1) allow commercial use; some models ship modified or research licenses.
- Verify the exact tag before pulling. Library names change between releases — confirm on ollama.com/library, and prefer explicit size tags over defaults.
- Outgrowing one machine? Price a dedicated GPU setup with our self-host cost calculator before renting API capacity.
Ollama model FAQ
What is the best Ollama model right now?
Kimi K2.6 is the highest-scoring open-weight model you can serve through Ollama, at 56.79 on the July 2026 leaderboard — but its 1T-parameter MoE needs roughly 600GB of memory at Q4, firmly server territory. For a machine you actually own, Qwen3.6-27B (53.82) is the best answer: its ~16GB Q4_K_M build fits a single 24GB GPU or a 32GB Mac and leads its size class on coding benchmarks.
What is the best Ollama model for coding?
Qwen3.6-27B is the best coding model for typical Ollama hardware, scoring 77.2 on SWE-bench Verified and 83.9 on LiveCodeBench from a single 24GB GPU. Kimi K2.6 leads all open weights (80.2 SWE-bench Verified, 89.6 LiveCodeBench) if you have a multi-GPU server. On an 8GB card, JetBrains' Mellum2-12B (69.9 LiveCodeBench) is the strongest dedicated coding pull.
How much RAM or VRAM do I need for Ollama?
Budget roughly 0.6GB per billion parameters at Q4_K_M, plus 20–30% headroom for context. In practice: 8GB runs 7–12B models (Gemma 4 12B, Mellum2), 16GB handles the 14–20B class, and 24GB fits 27–31B picks like Qwen3.6-27B and Gemma 4 31B. Macs count unified memory the same way. Beyond ~32GB you are into multi-GPU rigs or high-memory Mac Studios — see our local LLM guide by VRAM tier.
Are Ollama models free?
Yes. Ollama itself is free, open-source software, and every model in its library is open-weight, so downloading and running them costs nothing beyond your hardware and electricity — no per-token fees. Licenses still vary: Qwen3.6-27B and Mistral Small 4 are Apache-2.0 and GLM-5.1 is MIT (all commercial-friendly), while models like Kimi K2.6 use modified licenses. Check the license before shipping a commercial product on top of one.
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