Mistral Large vs Qwen2.5-72B-Instruct
Mistral Large (2024) and Qwen2.5-72B-Instruct (2024) are compact production models from MistralAI and Alibaba. Mistral Large ships a 32k-token context window, while Qwen2.5-72B-Instruct ships a 128K-token context window. On pricing, Qwen2.5-72B-Instruct costs $0.18/1M input tokens versus $0.32/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Qwen2.5-72B-Instruct is ~78% cheaper at $0.18/1M; pay for Mistral Large only for vision-heavy evaluation.
Decision scorecard
Local evidence first| Signal | Mistral Large | Qwen2.5-72B-Instruct |
|---|---|---|
| Best for | multimodal apps, tool-calling agents, and provider-routed production | provider-routed production |
| Decision fit | Agents, Vision, and Classification | Coding, RAG, and Long context |
| Context window | 32k | 128K |
| Cheapest output | $0.96/1M tokens | $0.54/1M tokens |
| Provider routes | 8 tracked | 7 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Mistral Large has broader tracked provider coverage for fallback and procurement flexibility.
- Mistral Large uniquely exposes Vision, Function calling, and Tool use in local model data.
- Local decision data tags Mistral Large for Agents, Vision, and Classification.
- Qwen2.5-72B-Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qwen2.5-72B-Instruct has the lower cheapest tracked output price at $0.54/1M tokens.
- Local decision data tags Qwen2.5-72B-Instruct for Coding, RAG, 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-Instruct
$279
Cheapest tracked route/tier: Chutes AI
Estimated monthly gap: $217. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter and Fireworks AI; start route-level A/B tests there.
- Qwen2.5-72B-Instruct is $0.42/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Vision, Function calling, and Tool use before moving production traffic.
- Provider overlap exists on OpenRouter and Fireworks AI; start route-level A/B tests there.
- Mistral Large is $0.42/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Mistral Large adds Vision, Function calling, and Tool use in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-02-08 | 2024-06-07 |
| Context window | 32k | 128K |
| Parameters | 123B | 72.7B |
| Architecture | - | decoder only |
| License | Proprietary | Apache 2.0 |
| Knowledge cutoff | 2024-03 | - |
Pricing and availability
| Pricing attribute | Mistral Large | Qwen2.5-72B-Instruct |
|---|---|---|
| Input price | $0.32/1M tokens | $0.18/1M tokens |
| Output price | $0.96/1M tokens | $0.54/1M tokens |
| Providers |
Capabilities
| Capability | Mistral Large | Qwen2.5-72B-Instruct |
|---|---|---|
| Vision | Yes | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: Mistral Large, function calling: Mistral Large, and tool use: Mistral Large. Both models share structured outputs, 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.
For cost, Mistral Large lists $0.32/1M input and $0.96/1M output tokens on the cheapest tracked provider, while Qwen2.5-72B-Instruct lists $0.18/1M input and $0.54/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen2.5-72B-Instruct lower by about $0.22 per million blended tokens. Availability is 8 providers versus 7, so concentration risk also matters.
Choose Mistral Large when vision-heavy evaluation and broader provider choice are central to the workload. Choose Qwen2.5-72B-Instruct when long-context analysis, larger context windows, and lower input-token cost 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 or Qwen2.5-72B-Instruct?
Qwen2.5-72B-Instruct 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.
Which is cheaper, Mistral Large or Qwen2.5-72B-Instruct?
Qwen2.5-72B-Instruct is cheaper on tracked token pricing. Mistral Large costs $0.32/1M input and $0.96/1M output tokens. Qwen2.5-72B-Instruct costs $0.18/1M input and $0.54/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Mistral Large or Qwen2.5-72B-Instruct open source?
Mistral Large is listed under Proprietary. Qwen2.5-72B-Instruct 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, Mistral Large or Qwen2.5-72B-Instruct?
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-Instruct?
Mistral Large 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 Mistral Large and Qwen2.5-72B-Instruct?
Mistral Large is available on NVIDIA NIM, Microsoft Foundry, AWS Bedrock, Mistral AI Studio, and IBM watsonx. Qwen2.5-72B-Instruct is available on DeepInfra, OpenRouter, Fireworks AI, Novita AI, and Chutes AI. 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.