Mistral Large vs Qwen2.5-72B
Mistral Large (2024) and Qwen2.5-72B (2025) are compact production models from MistralAI and Alibaba. Mistral Large ships a 32k-token context window, while Qwen2.5-72B ships a 128k-token context window. On MMLU PRO, Qwen2.5-72B leads by 20.5 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. The goal is to make the tradeoff clear before deeper testing.
Qwen2.5-72B fits 4x more tokens; pick it for long-context work and Mistral Large for tighter calls.
Decision scorecard
Local evidence first| Signal | Mistral Large | Qwen2.5-72B |
|---|---|---|
| Best for | multimodal apps, tool-calling agents, and provider-routed production | tool-calling agents |
| Decision fit | Agents, Vision, and Classification | RAG, Agents, and Long context |
| Context window | 32k | 128k |
| Cheapest output | $0.96/1M tokens | - |
| Provider routes | 8 tracked | 0 tracked |
| Shared benchmarks | 1 rows | MMLU PRO leader |
Decision tradeoffs
- Mistral Large has broader tracked provider coverage for fallback and procurement flexibility.
- Mistral Large uniquely exposes Vision and Structured outputs in local model data.
- Local decision data tags Mistral Large for Agents, Vision, and Classification.
- Qwen2.5-72B leads the largest shared benchmark signal on MMLU PRO by 20.5 points.
- Qwen2.5-72B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- 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.
Mistral Large
$496
Cheapest tracked route/tier: GCP Vertex 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 Mistral Large and Qwen2.5-72B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Vision and Structured outputs before moving production traffic.
- No overlapping tracked provider route is sourced for Qwen2.5-72B and Mistral Large; plan for SDK, billing, or endpoint changes.
- Mistral Large adds Vision and Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-02-08 | 2025-10-10 |
| Context window | 32k | 128k |
| Parameters | 123B | 72B |
| Architecture | - | - |
| License | Proprietary | Open Source |
| Knowledge cutoff | 2024-03 | 2024-09 |
Pricing and availability
| Pricing attribute | Mistral Large | Qwen2.5-72B |
|---|---|---|
| Input price | $0.32/1M tokens | - |
| Output price | $0.96/1M tokens | - |
| Providers | - |
Capabilities
| Capability | Mistral Large | Qwen2.5-72B |
|---|---|---|
| Vision | Yes | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | Yes | Yes |
| Tool use | Yes | Yes |
| Structured outputs | Yes | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | Mistral Large | Qwen2.5-72B |
|---|---|---|
| MMLU PRO | 51.5 | 72.0 |
Deep dive
On shared benchmark coverage, MMLU PRO has Mistral Large at 51.5 and Qwen2.5-72B at 72, with Qwen2.5-72B ahead by 20.5 points. The largest visible gap is 20.5 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 vision: Mistral Large and structured outputs: Mistral Large. 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: Mistral Large has $0.32/1M 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 Mistral Large when vision-heavy evaluation 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, Mistral Large or Qwen2.5-72B?
Qwen2.5-72B supports 128k tokens, while Mistral Large supports 32k 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 Mistral Large or Qwen2.5-72B open source?
Mistral Large is listed under Proprietary. 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 vision, Mistral Large or Qwen2.5-72B?
Mistral Large 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 function calling, Mistral Large or Qwen2.5-72B?
Both Mistral Large 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, Mistral Large or Qwen2.5-72B?
Both Mistral Large 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.
Where can I run Mistral Large and Qwen2.5-72B?
Mistral Large is available on NVIDIA NIM, Microsoft Foundry, AWS Bedrock, Mistral AI Studio, and IBM watsonx. Qwen2.5-72B is available on the tracked providers still being sourced. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.