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. Both come from Chinese companies. Both use Mixture-of-Experts architecture. Both are cheap enough to make you question why you’re paying for Claude or GPT.
But they’re built differently and optimized for different things.
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) |
| HumanEval | ~86% | 82.6% (89.1% V3-0324) |
| MMLU | 88.6% | 81.2% (V3-0324) |
| AIME 2026 | 91.3 | 59.4 (V3-0324) |
| API input price | ~$0.11/M | $0.27/M |
| API output price | ~$0.11/M | $1.10/M |
| License | Apache 2.0 | MIT |
| Training cost | Undisclosed | ~$5.5M |
| Release | Feb 16, 2026 | Dec 25, 2024 (updated Mar 2025) |
Where Qwen 3.5 wins
Benchmarks. Qwen 3.5 leads on almost every major benchmark. MMLU (88.6 vs 81.2), AIME 2026 (91.3 vs 59.4), SWE-bench (76.4 vs ~70), instruction following (IFBench 76.5 — highest of any model). The gap on math reasoning is particularly large.
Multimodal. Qwen 3.5 is natively multimodal — it processes text, images, and video. DeepSeek V3 is text-only. For document understanding, chart analysis, or any visual task, Qwen is the only option.
201 languages. Qwen supports 201 languages and dialects. DeepSeek focuses primarily on English and Chinese. For multilingual applications, Qwen is far more capable.
Larger context. 256K native (1M via API) vs 128K. For long documents or large codebases, Qwen holds more.
Cheaper. Qwen’s API costs ~$0.11/M input tokens vs DeepSeek’s $0.27/M. On output, the gap is even larger: $0.11 vs $1.10. Qwen is roughly 10x cheaper on output tokens.
Model family. Qwen 3.5 comes in 8 sizes from 0.8B to 397B. DeepSeek V3 is a single model. If you need a tiny model for edge deployment, Qwen has options.
Where DeepSeek V3 wins
Training efficiency. DeepSeek V3 was trained for approximately $5.5 million — a fraction of what comparable models cost. This matters because it proved that frontier-level AI doesn’t require billion-dollar budgets, which has implications for the entire industry.
MIT license. DeepSeek uses the MIT license, which is slightly more permissive than Apache 2.0 in some edge cases. Both are very open, but MIT has fewer requirements.
Coding depth. While Qwen scores higher on benchmarks, DeepSeek has a strong reputation in the developer community for practical coding quality. Many developers report that DeepSeek “feels” better for real-world coding tasks, even when benchmark numbers favor Qwen. This is subjective but worth noting.
Ecosystem and community. DeepSeek has a massive community of developers and a strong presence on platforms like OpenRouter. The DeepSeek API is well-documented and widely integrated.
Reasoning model. DeepSeek also offers R1, a dedicated reasoning model comparable to OpenAI’s o1 at 90-95% lower cost. Qwen doesn’t have a direct equivalent (though Qwen 3.5’s thinking mode serves a similar purpose).
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 vs February 2026) and hasn’t been updated to match Qwen 3.5’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 when you need dedicated reasoning.