DeepSeek V4 is the capability flagship; Qwen3.5 is the efficiency and ecosystem play. V4 wins on peak reasoning and its 1M context. Qwen counters with Apache 2.0 (the cleaner enterprise license), 17B-active serving economics, and a family that scales from 9B to 397B — which matters enormously if you fine-tune or deploy across device classes. Most organizations end up using both: DeepSeek at the frontier, Qwen everywhere else.
Qwen vs DeepSeek at a glance
| Qwen3.5 | DeepSeek V4 | |
|---|---|---|
| Vendor | Alibaba (Qwen team) (China) | DeepSeek (China) |
| Open weights | Yes — downloadable | Yes — downloadable |
| License | Apache 2.0 — permissive, patent-granting, free for commercial use | MIT — weights free to download, self-host, fine-tune, and use commercially |
| Parameters | 397B (17B active) | 1.6T (49B active) |
| Context window | 262K tokens (native) | 1M tokens |
| Modalities | text | text |
| Pricing | Varies by hosting provider (open weights) | $1.74 in / $3.48 out per 1M tokens |
| Released | 2026-02-16 | 2026-04-24 |
Specs and pricing verified July 2026.
About Qwen3.5
Qwen3.5 is Alibaba's February 2026 generation and the efficiency benchmark of the open-weight field: the 397B-A17B flagship activates just 17B parameters per token yet beats Alibaba's own trillion-parameter Qwen3-Max — at 8.6× the throughput at 32K context and 19× at 256K. All open-weight variants ship under Apache 2.0, the most enterprise-friendly license in common use.
The release is a full family, not one model: from the 397B flagship down to a 9B variant with a native 256K context window that runs on consumer hardware. That spread — one architecture, Apache 2.0 everywhere, sizes from phone to datacenter — is why Qwen has become the default base-model family for fine-tuners and the most-adopted open-weight line by download count.
Full specs, benchmarks, and hardware guidance: the Qwen3.5 page.
About DeepSeek V4
DeepSeek V4 Pro is the flagship of DeepSeek's V4 family, released on April 24, 2026, and the strongest argument yet that open-weight models compete at the frontier. It is a 1.6-trillion-parameter mixture-of-experts model that activates just 49B parameters per token, pairs a 1M-token context window with up to 384K tokens of output, and ships under a plain MIT license — no usage thresholds, no acceptable-use gate.
The headline engineering story is the attention stack. V4 combines Compressed Sparse Attention (CSA), which compresses KV entries 4× along the sequence with softmax-gated pooling, with Heavily Compressed Attention (HCA) at 128× compression. The practical result: at 1M-token context, V4 Pro needs roughly 27% of the single-token inference FLOPs and 10% of the KV cache of DeepSeek-V3.2. Long context stopped being a luxury and became the default across DeepSeek's services.
Full specs, benchmarks, and hardware guidance: the DeepSeek V4 page.
Choosing between them
Choose Qwen3.5 for:
- Enterprise deployments that require Apache 2.0 licensing
- Efficiency-critical serving (17B active parameters, extreme throughput)
- Fine-tuning projects — the broadest size ladder in open weights
Choose DeepSeek V4 for:
- Frontier-level reasoning and coding on an open license
- Very long documents and repositories (1M-token context as the default)
- Teams that need Anthropic/OpenAI API compatibility with open-weight economics
Frequently asked questions
Is Qwen3.5 better than DeepSeek V4?
DeepSeek V4 is the capability flagship; Qwen3.5 is the efficiency and ecosystem play. V4 wins on peak reasoning and its 1M context. Qwen counters with Apache 2.0 (the cleaner enterprise license), 17B-active serving economics, and a family that scales from 9B to 397B — which matters enormously if you fine-tune or deploy across device classes. Most organizations end up using both: DeepSeek at the frontier, Qwen everywhere else.
Which is cheaper: Qwen3.5 or DeepSeek V4?
Qwen3.5: Varies by hosting provider (open weights). DeepSeek V4: $1.74 in / $3.48 out per 1M tokens. Both are open-weight, so self-hosting costs depend on your hardware and utilization.
Can I self-host Qwen3.5 and DeepSeek V4?
Qwen3.5: yes — weights are published under Apache 2.0. DeepSeek V4: yes — weights are published under MIT.