Short answer (June 2026): DeepSeek V4 is the top open-weight model overall and the strongest for agentic work. Qwen 3.5 is the safest enterprise pick thanks to its Apache-2.0 license and broad ecosystem. Llama 4 is unmatched for ultra-long context. Mistral trails the frontier but ships clean Apache-2.0 licensing. If raw capability is all that matters, Kimi K2.6 currently edges them all.
- Best open-weight overall & agentic: DeepSeek V4
- Best for enterprise / licensing: Qwen 3.5 (Apache-2.0)
- Best long context: Llama 4 (Scout, up to 10M tokens)
- Top raw capability: Kimi K2.6
At a glance
| Model | Best for | License | Standout | Watch-out |
|---|---|---|---|---|
| DeepSeek V4 | Agentic, general | MIT | #1 open-weight, 1M-token context, multimodal | Large; needs serious GPU |
| Qwen 3.5 | Enterprise, multilingual | Apache-2.0 | Commercial flexibility, huge fine-tune ecosystem | Not always #1 on raw benchmarks |
| Llama 4 | Long context | Llama license | Scout's 10M-token context, high MMLU | 700M MAU cap + EU restrictions |
| Mistral Large 3 | Lightweight, permissive | Apache-2.0 | Clean licensing, efficient | Behind the frontier on top scores |
How we compared
We weighed capability (benchmarks, agentic ability), context length, licensing freedom, and self-hosting practicality. Figures reflect the open-weight landscape as of June 2026 and move fast.
DeepSeek V4
DeepSeek released V4 Pro and V4 Flash in April 2026, both MIT-licensed with a 1M-token context. V4 is a ~1-trillion-parameter mixture-of-experts model (~32–37B active per token) with native multimodal generation. It ranks #1 among open-weight models for agentic tasks.
DeepSeek V4 is the best open-weight model in 2026 if you have the hardware to run it. That scale is also the catch — it demands serious GPU resources to self-host well.
Qwen 3.5
Qwen 3.5 is the safest enterprise choice: Apache-2.0 licensed, strong on multilingual tasks, and backed by the broadest ecosystem of fine-tunes. The mixture-of-experts variants give you commercial flexibility with zero royalties.
Pick Qwen 3.5 when licensing clarity, multilingual support, and ecosystem matter more than topping a single benchmark. It isn't always the raw-capability leader, but it's the most dependable for production.
Llama 4
Llama 4 Maverick posts one of the highest MMLU scores among open models, and Llama 4 Scout's 10M-token context is unmatched for long-document work. The ecosystem and tooling around Llama remain enormous.
Choose Llama 4 when ultra-long context is the requirement. Read the license carefully, though — the 700M monthly-active-user cap and EU restrictions matter for larger deployments.
Mistral
Mistral Large 3 and Mistral Small 4 now ship under Apache-2.0, a major shift from Mistral's earlier restrictive terms. They're efficient and easy to deploy, even if they trail the absolute frontier on top benchmark scores.
Mistral is a strong pick for lightweight, permissively licensed deployments. If you need frontier-level capability, look to DeepSeek or Kimi instead.
Which open-source LLM should you choose?
- Maximum open-weight capability, have GPUs → DeepSeek V4 (or Kimi K2.6).
- Enterprise, commercial use, multilingual → Qwen 3.5 (Apache-2.0).
- Long documents / huge context → Llama 4 Scout.
- Lightweight, permissive, easy to run → Mistral.
- Just want to run locally fast → any of these pull with a single Ollama command; stick to <8B on CPU-only.
Frequently asked questions
What is the best open-source LLM in 2026?
DeepSeek V4 is the best open-weight model overall and #1 for agentic tasks. Kimi K2.6 currently edges it on raw capability, while Qwen 3.5 is the safest enterprise pick.
Which open-source LLM has the most permissive license?
Qwen 3.5 (Apache-2.0), DeepSeek V4 (MIT), and GLM-5 (MIT) are the most permissive — free for commercial use and fine-tuning with no royalties.
What's the best open LLM for long context?
Llama 4 Scout, with a context window up to 10M tokens, is unmatched for long-document and long-context work.
Can I self-host these models easily?
Yes — every major model here can be pulled and run with a single Ollama command. On a GPU it's fast; on CPU-only machines, stick to models under 8B parameters.
Conclusion
Open-weight models are genuinely competitive with closed frontier models in 2026. DeepSeek V4 leads capability, Qwen 3.5 wins on licensing and enterprise fit, Llama 4 owns long context, and Mistral keeps things lightweight and permissive. Which one are you self-hosting? Share your setup in the comments.
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