Capability · Comparison
Qwen 3 vs DeepSeek V3
Qwen 3 (Alibaba) and DeepSeek V3 (DeepSeek AI) are the two most important open-weights Chinese frontier models. Both are strong, both are open-license-friendly, and both are serious alternatives to Llama at the large-model tier. Qwen 3 is broader and multilingual; DeepSeek V3 is a sparse Mixture-of-Experts (MoE) that trades less compute for strong reasoning.
Side-by-side
| Criterion | Qwen 3 | DeepSeek V3 |
|---|---|---|
| Architecture | Dense + MoE variants (0.5B to 235B) | MoE (671B params, ~37B active) |
| License | Apache 2.0 (most variants) | DeepSeek License (open weights, commercial allowed) |
| Context window | 128,000 tokens | 128,000 tokens |
| Reasoning benchmarks | Strong | Very strong — competitive with GPT-4o class |
| Coding benchmarks | Strong | Very strong |
| Multilingual support | Excellent (119+ languages) | Good, English-first |
| Hosted API pricing (as of 2026-04) | ~$0.50/M input via DashScope / Together | ~$0.27/M input on DeepSeek API |
| Self-host compute | Options at every size from 0.5B up | Needs multi-GPU for full 671B |
| Ecosystem support | vLLM, SGLang, TRT-LLM, Ollama, llama.cpp | vLLM, SGLang (needs patches for MoE efficiency) |
Verdict
Qwen 3 is the more flexible family — it ships sizes from 0.5B to 235B under Apache 2.0, supports over 100 languages well, and is a drop-in replacement for Llama in most OSS pipelines. DeepSeek V3 is the reasoning champion of open-weights: the MoE design means 37B active params per token, so inference is cheap for its quality, and its benchmarks rival GPT-4o on reasoning. If you need multilingual or small variants, pick Qwen 3. If you need the most reasoning per dollar on a single model and can run MoE infra, pick DeepSeek V3.
When to choose each
Choose Qwen 3 if…
- You need strong multilingual support (esp. non-English Asian languages).
- You want small variants (0.5B-7B) as well as large.
- You want an Apache 2.0 license specifically.
- You're building an OSS-first stack with broad ecosystem support.
Choose DeepSeek V3 if…
- You need the strongest open-weights reasoning as of 2026-04.
- You want MoE efficiency (low compute per token for the quality).
- You can deploy on multi-GPU infra with MoE-aware serving.
- You're budget-constrained on API costs — DeepSeek's hosted API is the cheapest frontier-tier option.
Frequently asked questions
Is DeepSeek V3 really free to use commercially?
Under the DeepSeek License, yes — commercial use of weights is allowed. Some teams still prefer Apache 2.0 (Qwen) for legal simplicity. Always consult legal on license specifics for your use case.
How do I self-host DeepSeek V3's 671B MoE?
Realistically you need 8x H100 80GB (or larger) with vLLM or SGLang builds that have MoE-aware kernels. The MoE design means you run fewer active params per token, but memory footprint is still large. Most teams start with hosted endpoints (Together, Fireworks, DeepSeek's own API).
Which is faster in practice?
At equivalent hardware DeepSeek V3 MoE is often faster per token than a dense Qwen 3 235B because it only activates ~37B params per token. For small models (Qwen 3 7B etc.) Qwen wins by virtue of being tiny.
Sources
- Alibaba — Qwen model card — accessed 2026-04-20
- DeepSeek V3 technical report — accessed 2026-04-20