DeepSeek V4 Pro vs Qwen2.5-72B
DeepSeek V4 Pro (2026) and Qwen2.5-72B (2025) are frontier reasoning models from DeepSeek and Alibaba. DeepSeek V4 Pro ships a 1M-token context window, while Qwen2.5-72B ships a 128k-token context window. On MMLU PRO, DeepSeek V4 Pro leads by 15.5 pts. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
DeepSeek V4 Pro fits 8x more tokens; pick it for long-context work and Qwen2.5-72B for tighter calls.
Specs
| Released | 2026-04-24 | 2025-10-10 |
| Context window | 1M | 128k |
| Parameters | 1.6T | 72B |
| Architecture | mixture of experts | - |
| License | MIT | Open Source |
| Knowledge cutoff | - | 2024-09 |
Pricing and availability
| DeepSeek V4 Pro | Qwen2.5-72B | |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers | - | - |
Pricing not yet sourced for either model.
Capabilities
| DeepSeek V4 Pro | Qwen2.5-72B | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | DeepSeek V4 Pro | Qwen2.5-72B |
|---|---|---|
| MMLU PRO | 87.5 | 72.0 |
Deep dive
On shared benchmark coverage, MMLU PRO has DeepSeek V4 Pro at 87.5 and Qwen2.5-72B at 72, with DeepSeek V4 Pro ahead by 15.5 points. The largest visible gap is 15.5 points on MMLU PRO, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on reasoning mode: DeepSeek V4 Pro and structured outputs: DeepSeek V4 Pro. Both models share function calling and tool use, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
Pricing coverage is uneven: DeepSeek V4 Pro has no token price sourced yet and Qwen2.5-72B has no token price sourced yet. Provider availability is 0 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose DeepSeek V4 Pro when reasoning depth and larger context windows are central to the workload. Choose Qwen2.5-72B when provider fit are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, DeepSeek V4 Pro or Qwen2.5-72B?
DeepSeek V4 Pro supports 1M tokens, while Qwen2.5-72B supports 128k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is DeepSeek V4 Pro or Qwen2.5-72B open source?
DeepSeek V4 Pro is listed under MIT. Qwen2.5-72B is listed under Open Source. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for reasoning mode, DeepSeek V4 Pro or Qwen2.5-72B?
DeepSeek V4 Pro has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for function calling, DeepSeek V4 Pro or Qwen2.5-72B?
Both DeepSeek V4 Pro and Qwen2.5-72B expose function calling. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for tool use, DeepSeek V4 Pro or Qwen2.5-72B?
Both DeepSeek V4 Pro and Qwen2.5-72B expose tool use. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
When should I pick DeepSeek V4 Pro over Qwen2.5-72B?
DeepSeek V4 Pro fits 8x more tokens; pick it for long-context work and Qwen2.5-72B for tighter calls. If your workload also depends on reasoning depth, start with DeepSeek V4 Pro; if it depends on provider fit, run the same evaluation with Qwen2.5-72B.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.