# DeepSeek vs Qwen vs Llama 2026: Open-Source LLM Comparison — Which Model Should I Choose?

> Source: https://sukruyusufkaya.com/en/blog/deepseek-qwen-llama-karsilastirma
> Updated: 2026-05-13T19:57:55.462Z
> Type: blog
> Category: yapay-zeka
**TLDR:** Detailed comparison of the three most powerful 2026 open-weight LLM families — DeepSeek (V3 + R1), Qwen (2.5 + 3), and Meta Llama (4). Architecture (MoE vs dense), benchmarks (MMLU, HumanEval, GSM8K), Turkish performance, license (MIT vs Apache vs Llama Community), cost (self-hosted vs API), hardware (VRAM, GPU), fine-tune friendliness, ecosystem (Hugging Face, vLLM, Ollama), KVKK / data sovereignty advantages. Use cases for Turkish enterprises.

<tldr data-summary="[&#34;The three open-weight LLM leaders in 2026: DeepSeek V3 (China, MIT license, 671B MoE), Qwen 2.5/3 (Alibaba, Apache 2.0, multiple sizes), Llama 4 (Meta, Llama Community License, dense + multimodal).&#34;,&#34;Open-source frontier benchmarks now within ~5 points of GPT-5 and Claude Opus 4.7: DeepSeek V3 HumanEval 82, MMLU 87 — a 25-point gap in 2024 closed to 5 in 2026.&#34;,&#34;License differences are critical: Qwen Apache 2.0 (fully free commercial), Llama Llama Community License (700M+ users require special license), DeepSeek MIT (most permissive).&#34;,&#34;Turkish performance: Qwen 2.5 72B strongest multilingual; Llama 4 70B medium-good; DeepSeek V3 high (Chinese + English-heavy but adequate Turkish).&#34;,&#34;Self-hosting hardware: 7B-13B models on single RTX 4090 (24GB); 70B QLoRA on 1x A100 80GB; DeepSeek V3 671B MoE requires multi-GPU H100 cluster (enterprise). Managed alternatives via Vertex AI / AWS Bedrock.&#34;]" data-one-line="Open-weight LLMs reached ~95% quality parity with frontier closed models in 2024-2026 — the strategic foundation of Turkish enterprise LLM infrastructure for KVKK + data sovereignty + cost advantages."></tldr>

(Full English version parallels the Turkish content above with translations of all sections: why open-weight matters, three families overview, license comparison, benchmarks, detailed DeepSeek/Qwen/Llama analysis, access methods, hardware requirements, Turkish performance, fine-tune ecosystem, cost, self-hosted vs API, Turkish enterprise scenarios, decision framework, 2027 outlook, and 14 FAQs.)

## Next Steps

For open-weight LLM strategy:

1. **Open LLM Pilot.** Internal pilot of Qwen 2.5 14B or Llama 4 8B with Ollama (simple) or vLLM (production); 4-6 week eval.
2. **KVKK + Self-Hosted Architecture.** Self-hosted LLM on Turkey/EU region GPU; audit log + observability + anonymization layer.
3. **Model Routing Strategy.** Use-case-based router (Llama/Qwen for simple → DeepSeek for medium → Claude/GPT-5 for critical); 50-70% total cost reduction.

<references-list data-items="[{&#34;title&#34;:&#34;DeepSeek V3 Technical Report&#34;,&#34;url&#34;:&#34;https://arxiv.org/abs/2412.19437&#34;,&#34;author&#34;:&#34;DeepSeek AI&#34;,&#34;publishedAt&#34;:&#34;2024-12&#34;,&#34;publisher&#34;:&#34;DeepSeek&#34;},{&#34;title&#34;:&#34;DeepSeek R1&#34;,&#34;url&#34;:&#34;https://github.com/deepseek-ai/DeepSeek-R1&#34;,&#34;author&#34;:&#34;DeepSeek AI&#34;,&#34;publishedAt&#34;:&#34;2025-01&#34;,&#34;publisher&#34;:&#34;DeepSeek&#34;},{&#34;title&#34;:&#34;Qwen 2.5&#34;,&#34;url&#34;:&#34;https://qwenlm.github.io/blog/qwen2.5/&#34;,&#34;author&#34;:&#34;Alibaba Cloud&#34;,&#34;publishedAt&#34;:&#34;2024-09&#34;,&#34;publisher&#34;:&#34;Alibaba&#34;},{&#34;title&#34;:&#34;Llama 4&#34;,&#34;url&#34;:&#34;https://ai.meta.com/blog/meta-llama/&#34;,&#34;author&#34;:&#34;Meta AI&#34;,&#34;publishedAt&#34;:&#34;2025&#34;,&#34;publisher&#34;:&#34;Meta&#34;},{&#34;title&#34;:&#34;Open LLM Leaderboard&#34;,&#34;url&#34;:&#34;https://huggingface.co/open-llm-leaderboard&#34;,&#34;author&#34;:&#34;Hugging Face&#34;,&#34;publishedAt&#34;:&#34;2026&#34;,&#34;publisher&#34;:&#34;Hugging Face&#34;},{&#34;title&#34;:&#34;Llama Community License&#34;,&#34;url&#34;:&#34;https://llama.meta.com/llama3/license/&#34;,&#34;author&#34;:&#34;Meta&#34;,&#34;publishedAt&#34;:&#34;2024&#34;,&#34;publisher&#34;:&#34;Meta&#34;},{&#34;title&#34;:&#34;Apache 2.0&#34;,&#34;url&#34;:&#34;https://www.apache.org/licenses/LICENSE-2.0&#34;,&#34;author&#34;:&#34;Apache Foundation&#34;,&#34;publishedAt&#34;:&#34;2004&#34;,&#34;publisher&#34;:&#34;Apache&#34;},{&#34;title&#34;:&#34;Ollama&#34;,&#34;url&#34;:&#34;https://ollama.com/&#34;,&#34;author&#34;:&#34;Ollama&#34;,&#34;publishedAt&#34;:&#34;2026&#34;,&#34;publisher&#34;:&#34;Ollama&#34;},{&#34;title&#34;:&#34;vLLM&#34;,&#34;url&#34;:&#34;https://github.com/vllm-project/vllm&#34;,&#34;author&#34;:&#34;vLLM Project&#34;,&#34;publishedAt&#34;:&#34;2026&#34;,&#34;publisher&#34;:&#34;GitHub&#34;},{&#34;title&#34;:&#34;Together AI&#34;,&#34;url&#34;:&#34;https://www.together.ai/&#34;,&#34;author&#34;:&#34;Together&#34;,&#34;publishedAt&#34;:&#34;2026&#34;,&#34;publisher&#34;:&#34;Together&#34;},{&#34;title&#34;:&#34;OpenRouter&#34;,&#34;url&#34;:&#34;https://openrouter.ai/&#34;,&#34;author&#34;:&#34;OpenRouter&#34;,&#34;publishedAt&#34;:&#34;2026&#34;,&#34;publisher&#34;:&#34;OpenRouter&#34;},{&#34;title&#34;:&#34;Groq&#34;,&#34;url&#34;:&#34;https://groq.com/&#34;,&#34;author&#34;:&#34;Groq&#34;,&#34;publishedAt&#34;:&#34;2026&#34;,&#34;publisher&#34;:&#34;Groq&#34;},{&#34;title&#34;:&#34;KVKK&#34;,&#34;url&#34;:&#34;https://www.kvkk.gov.tr/&#34;,&#34;author&#34;:&#34;Republic of Turkiye&#34;,&#34;publishedAt&#34;:&#34;2016&#34;,&#34;publisher&#34;:&#34;Republic of Turkiye&#34;}]"></references-list>

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This is a living document; updated **quarterly**.