# Best Open-Source LLM in 2026: DeepSeek V4 vs Llama 4 vs Qwen 3.5 vs Mistral > Published 2026-06-22 · https://www.promptzone.com/farrah_dubois/best-open-source-llm-in-2026-deepseek-v4-vs-llama-4-vs-qwen-35-vs-mistral-27al --- title: Best Open-Source LLM in 2026: DeepSeek V4 vs Llama 4 vs Qwen 3.5 vs Mistral published: true description: A 2026 comparison of the best open-source LLMs — DeepSeek V4, Llama 4, Qwen 3.5 and Mistral — with verdict, licensing, and self-hosting picks. tags: ai, opensource, llm, comparison --- **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. ## Sources - [Hugging Face — Best Open-Source LLMs 2026](https://huggingface.co/blog/daya-shankar/open-source-llms) - [Codersera — Best Open-Source LLM 2026](https://codersera.com/blog/best-open-source-llm-2026-llama-4-qwen-3-5-deepseek-v4-gemma-4-mistral/) - [ComputingForGeeks — Open Source LLM Comparison](https://computingforgeeks.com/open-source-llm-comparison/)