DeepSeek V3.1 vs Qwen2.5-72B
DeepSeek V3.1 (2025) and Qwen2.5-72B (2025) are compact production models from DeepSeek and Alibaba. DeepSeek V3.1 ships a 64k-token context window, while Qwen2.5-72B ships a 128k-token context window. On MMLU PRO, DeepSeek V3.1 leads by 11.3 pts. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads. It focuses on practical selection signals rather than broad model-family marketing.
Qwen2.5-72B is safer overall; choose DeepSeek V3.1 when coding workflow support matters.
Decision scorecard
Local evidence first| Signal | DeepSeek V3.1 | Qwen2.5-72B |
|---|---|---|
| Best for | multimodal apps and provider-routed production | tool-calling agents |
| Decision fit | Coding, Agents, and Vision | RAG, Agents, and Long context |
| Context window | 64k | 128k |
| Cheapest output | $1/1M tokens | - |
| Provider routes | 8 tracked | 0 tracked |
| Shared benchmarks | MMLU PRO leader | 1 rows |
Decision tradeoffs
- DeepSeek V3.1 holds a shared-benchmark lead on MMLU PRO, ahead by 11.3 points.
- DeepSeek V3.1 has broader tracked provider coverage for fallback and procurement flexibility.
- DeepSeek V3.1 uniquely exposes Vision, Multimodal, and Structured outputs in local model data.
- Local decision data tags DeepSeek V3.1 for Coding, Agents, and Vision.
- Qwen2.5-72B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qwen2.5-72B uniquely exposes Function calling and Tool use in local model data.
- Local decision data tags Qwen2.5-72B for RAG, Agents, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
DeepSeek V3.1
$466
Cheapest tracked route/tier: Novita AI
Qwen2.5-72B
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for DeepSeek V3.1 and Qwen2.5-72B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Vision, Multimodal, and Structured outputs before moving production traffic.
- Qwen2.5-72B adds Function calling and Tool use in local capability data.
- No overlapping tracked provider route is sourced for Qwen2.5-72B and DeepSeek V3.1; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Function calling and Tool use before moving production traffic.
- DeepSeek V3.1 adds Vision, Multimodal, and Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-08-21 | 2025-10-10 |
| Context window | 64k | 128k |
| Parameters | 671B total, 37B active (MoE) | 72B |
| Architecture | mixture of experts | - |
| License | MIT(OSI) | Apache 2.0(OSI) |
| Openness | Open source | Open source |
| Commercial use | Commercial use allowed | Commercial use allowed |
| Knowledge cutoff | - | 2024-09 |
Pricing and availability
| Pricing attribute | DeepSeek V3.1 | Qwen2.5-72B |
|---|---|---|
| Input price | $0.27/1M tokens | - |
| Output price | $1/1M tokens | - |
| Providers | - |
Capabilities
| Capability | DeepSeek V3.1 | Qwen2.5-72B |
|---|---|---|
| Vision | Yes | No |
| Multimodal | Yes | No |
| Reasoning | No | No |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | Yes | No |
| Code execution | Yes | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | DeepSeek V3.1 | Qwen2.5-72B |
|---|---|---|
| MMLU PRO | 83.3 | 72.0 |
Deep dive
On shared benchmark coverage, MMLU PRO has DeepSeek V3.1 at 83.3 and Qwen2.5-72B at 72, with DeepSeek V3.1 ahead by 11.3 points. The largest visible gap is 11.3 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 vision: DeepSeek V3.1, multimodal input: DeepSeek V3.1, function calling: Qwen2.5-72B, tool use: Qwen2.5-72B, structured outputs: DeepSeek V3.1, and code execution: DeepSeek V3.1. Both models share the core language-model surface, 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 V3.1 has $0.27/1M input tokens and Qwen2.5-72B has no token price sourced yet. Provider availability is 8 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 V3.1 when coding workflow support and broader provider choice are central to the workload. Choose Qwen2.5-72B when long-context analysis and larger context windows 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 V3.1 or Qwen2.5-72B?
Qwen2.5-72B supports 128k tokens, while DeepSeek V3.1 supports 64k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Is DeepSeek V3.1 or Qwen2.5-72B open source?
DeepSeek V3.1 is listed under MIT. Qwen2.5-72B is listed under Apache 2.0. 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 vision, DeepSeek V3.1 or Qwen2.5-72B?
DeepSeek V3.1 has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, DeepSeek V3.1 or Qwen2.5-72B?
DeepSeek V3.1 has the clearer documented multimodal input signal in this comparison. If multimodal input 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 V3.1 or Qwen2.5-72B?
Qwen2.5-72B has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run DeepSeek V3.1 and Qwen2.5-72B?
DeepSeek V3.1 is available on Microsoft Foundry, Fireworks AI, NVIDIA NIM, Together AI, and AWS Bedrock. Qwen2.5-72B is available on the tracked providers still being sourced. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.