DeepSeek V4-Pro vs Qwen3 235B-A22B — two open mixture-of-experts giants, side by side. Below is the full side-by-side: specifications, API pricing, context window, local hardware requirements, and a clear, data-driven recommendation on which to pick.
| Spec | DeepSeek V4-Pro | Qwen3 235B-A22B |
|---|---|---|
| Developer | DeepSeek | Alibaba |
| Type | LLM (MoE) | LLM (MoE) |
| Parameters | 1.6T total / ~49B active (MoE) | 235B total / 22B active (MoE) |
| Context window | 1M | 128K |
| Modality | Text → Text | Text → Text |
| License | MIT (open) | Apache 2.0 (open) |
| Open weights | ✅ Yes | ✅ Yes |
| Input price ($/1M) | $0.435 | $0.45 |
| Output price ($/1M) | $0.87 | $1.8 |
| VRAM (4-bit) | ~800 GB | ~140 GB |
| Min GPU (local) | Multi-GPU server (e.g. 8× H100 80GB) | Multi-GPU or Mac 192GB |
| Released | 2026-04 | 2025 |
Key differences
- Cost: DeepSeek V4-Pro is 45% cheaper than Qwen3 235B-A22B on a blended-token basis.
- Context: DeepSeek V4-Pro wins on context window (1M vs 128K) — better for long documents, large codebases and big RAG inputs.
- Openness: both are open-weight, so either can be self-hosted or fine-tuned. Compare their VRAM needs above to see what your GPU can run.
- Run DeepSeek V4-Pro locally: ~~800 GB at 4-bit (min Multi-GPU server (e.g. 8× H100 80GB)).
- Run Qwen3 235B-A22B locally: ~~140 GB at 4-bit (min Multi-GPU or Mac 192GB).
Which should you choose?
Choose DeepSeek V4-Pro if you want the lower per-token cost for high-volume workloads, or you need the larger context window.
Choose Qwen3 235B-A22B if it fits your existing stack or you prefer Alibaba.
→ Estimate real costs in the API cost calculator · check local hardware in the VRAM calculator · browse all 30+ models.
All specs and prices are pulled live from our AI models database and kept current. Compare either model against others, or estimate your own monthly spend with the free calculators above.