Qwen 3.5 vs DeepSeek V3 — The Two Best Open-Source AI Models Compared (2026)
Qwen 3.5 and DeepSeek V3 are the two most capable open-source AI models available today. Both come from Chinese companies, both use Mixture-of-Experts architecture, and both are cheap enough to make you question why you’re paying for proprietary alternatives.
Update (April 24, 2026): DeepSeek V4 is now available. See DeepSeek V4 vs Qwen 3.6-27B.
But they’re built differently and optimized for different strengths. This comparison breaks down benchmarks, pricing, coding quality, and practical considerations. For the full landscape, see our AI model comparison.
Quick Comparison
| Qwen 3.5-397B | DeepSeek V3 | |
|---|---|---|
| Company | Alibaba | DeepSeek |
| Total parameters | 397B | 671B |
| Active parameters | 17B | 37B |
| Context window | 256K (1M via API) | 128K |
| Multimodal | Yes (native vision) | No |
| Languages | 201 | ~30 |
| SWE-bench Verified | 76.4% | ~70% (V3.2) |
| MMLU | 88.6% | 81.2% |
| AIME 2026 | 91.3 | 59.4 |
| API input price | ~$0.11/M | $0.27/M |
| API output price | ~$0.11/M | $1.10/M |
| License | Apache 2.0 | MIT |
| Release | Feb 16, 2026 | Dec 25, 2024 (updated) |
Where Qwen 3.5 Wins
Benchmarks across the board. Qwen 3.5 leads on nearly every major benchmark: MMLU (88.6 vs 81.2), AIME 2026 (91.3 vs 59.4), SWE-bench (76.4 vs ~70), and instruction following (IFBench 76.5, the highest of any model). The math reasoning gap is particularly large.
Multimodal capabilities. Qwen 3.5 is natively multimodal — text, images, and video in a single model. DeepSeek V3 is text-only. For document understanding, chart analysis, or any visual task, Qwen is the only option.
Language support. Qwen covers 201 languages and dialects compared to DeepSeek’s roughly 30. For multilingual applications, Qwen is far more capable.
Larger context. 256K native (1M via API) versus 128K. For long documents or large codebases, Qwen holds significantly more.
Pricing. Qwen’s API costs ~$0.11/M input tokens versus DeepSeek’s $0.27/M. On output, the gap widens: $0.11 versus $1.10. Qwen is roughly 10x cheaper on output tokens.
If you want to self-host, see our guide on how to run Qwen 3.5 locally.
Model family. Qwen 3.5 comes in 8 sizes from 0.8B to 397B. DeepSeek V3 is a single model. For how Qwen compares to its successor, see Qwen 3.6 vs 3.5.
Where DeepSeek V3 Wins
Training efficiency. DeepSeek V3 was trained for approximately $5.5 million — a fraction of what comparable models cost. This proved frontier AI doesn’t require billion-dollar budgets.
MIT license. DeepSeek uses MIT, which is slightly more permissive than Apache 2.0 in some edge cases. Both are very open, but MIT has fewer requirements.
Practical coding feel. While Qwen scores higher on benchmarks, many developers report that DeepSeek produces more natural, idiomatic code for real-world tasks. This is subjective but consistently reported across forums and developer surveys.
Ecosystem and community. DeepSeek has a massive developer community and strong presence on platforms like OpenRouter. The API is well-documented and widely integrated.
For local deployment, check our how to run DeepSeek locally guide.
Dedicated reasoning model. DeepSeek offers R1, a dedicated reasoning model comparable to OpenAI’s o1 at 90-95% lower cost. Qwen’s thinking mode serves a similar purpose but isn’t a separate specialized model.
Running Both Locally
Both are open-source and available on HuggingFace. Hardware requirements differ:
- Qwen 3.5 smaller variants (0.8B–8B) run on consumer GPUs. The 32B and 72B need multi-GPU setups. The full 397B requires a cluster.
- DeepSeek V3 at 671B total parameters is very demanding. Quantized versions help but still require substantial hardware.
Both benefit from GGUF quantization and tools like llama.cpp or vLLM for efficient local inference.
Use Case Routing
For teams that want the best of both worlds, consider routing by task type:
- General reasoning, multimodal, multilingual → Qwen 3.5
- Coding tasks where you prefer DeepSeek’s style → DeepSeek V3
- Chain-of-thought reasoning → DeepSeek R1
- Edge deployment → Qwen 3.5 smaller variants (0.8B–8B)
This approach lets you leverage each model’s strengths without committing to just one.
The Honest Take
Qwen 3.5 is the better model on paper. It scores higher on more benchmarks, supports more languages, handles multimodal input, has a larger context window, and costs less per token.
DeepSeek V3 is older — December 2024 versus February 2026. It hasn’t been updated to match Qwen’s latest capabilities. When DeepSeek V4 launches, this comparison will likely shift.
For now: use Qwen 3.5 as your primary open-source model. Keep DeepSeek V3 as an alternative for coding tasks where you prefer its output style, or use DeepSeek R1 for dedicated reasoning.
Both models are excellent and represent the best of what open-source AI offers.
FAQ
Is Qwen 3.5 better than DeepSeek V3?
On benchmarks, yes. Qwen 3.5 leads on MMLU, AIME, SWE-bench, and instruction following. It also supports multimodal input, more languages, and a larger context window at lower cost. However, DeepSeek V3 is older and many developers prefer its coding output style for practical tasks.
Can I run both locally?
Yes, both are open-source and available on HuggingFace. The full models require significant hardware — Qwen 3.5-397B needs GPU clusters, and DeepSeek V3 at 671B is even more demanding. Qwen’s smaller variants (0.8B to 72B) run on consumer hardware. Both offer quantized versions for reduced requirements.
Which is better for coding?
Qwen 3.5 scores higher on coding benchmarks (76.4% vs ~70% on SWE-bench). However, many developers report that DeepSeek V3 produces more natural code in practice. For benchmark performance, Qwen wins. For subjective coding feel, opinions are split. Try both on your codebase.
Are both free?
Both are free to download and self-host under permissive licenses (Apache 2.0 for Qwen, MIT for DeepSeek). API access has costs, but both are among the cheapest frontier-tier models available. Qwen is approximately 10x cheaper on output tokens via API.