Mistral Large 2 (2407) vs Qwen2.5-72B
Mistral Large 2 (2407) (2024) and Qwen2.5-72B (2025) are compact production models from MistralAI and Alibaba. Mistral Large 2 (2407) ships a 128K-token context window, while Qwen2.5-72B ships a 128k-token context window. 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 is safer overall; choose Mistral Large 2 (2407) when vision-heavy evaluation matters.
Specs
| Released | 2024-07-23 | 2025-10-10 |
| Context window | 128K | 128k |
| Parameters | 123B | 72B |
| Architecture | decoder only | - |
| License | Apache 2.0 | Open Source |
| Knowledge cutoff | - | 2024-09 |
Pricing and availability
| Mistral Large 2 (2407) | Qwen2.5-72B | |
|---|---|---|
| Input price | $0.5/1M tokens | - |
| Output price | $1.5/1M tokens | - |
| Providers | - |
Capabilities
| Mistral Large 2 (2407) | Qwen2.5-72B | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: Mistral Large 2 (2407), function calling: Qwen2.5-72B, tool use: Qwen2.5-72B, and structured outputs: Mistral Large 2 (2407). 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: Mistral Large 2 (2407) has $0.5/1M input tokens and Qwen2.5-72B has no token price sourced yet. Provider availability is 3 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 2 (2407) when vision-heavy evaluation and broader provider choice 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. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency.
FAQ
Which has a larger context window, Mistral Large 2 (2407) or Qwen2.5-72B?
Mistral Large 2 (2407) supports 128K 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 Mistral Large 2 (2407) or Qwen2.5-72B open source?
Mistral Large 2 (2407) is listed under Apache 2.0. 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 2 (2407) or Qwen2.5-72B?
Mistral Large 2 (2407) 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.
Which is better for function calling, Mistral Large 2 (2407) 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.
Which is better for tool use, Mistral Large 2 (2407) or Qwen2.5-72B?
Qwen2.5-72B has the clearer documented tool use signal in this comparison. If tool use is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Mistral Large 2 (2407) and Qwen2.5-72B?
Mistral Large 2 (2407) is available on Microsoft Foundry, Chutes AI, and SiliconFlow. 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: 2026-04-23. Data sourced from public model cards and provider documentation.