Mistral Large vs o4-mini
Mistral Large (2024) and o4-mini (2025) are frontier reasoning models from MistralAI and OpenAI. Mistral Large ships a 32k-token context window, while o4-mini ships a not-yet-sourced context window. On MMLU PRO, o4-mini leads by 31.7 pts. On pricing, Mistral Large costs $0.32/1M input tokens versus $0.5/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Mistral Large is ~56% cheaper at $0.32/1M; pay for o4-mini only for coding workflow support.
Specs
| Released | 2024-02-08 | 2025-04-16 |
| Context window | 32k | — |
| Parameters | — | — |
| Architecture | - | decoder only |
| License | Proprietary | Proprietary |
| Knowledge cutoff | 2024-03 | 2025-08 |
Pricing and availability
| Mistral Large | o4-mini | |
|---|---|---|
| Input price | $0.32/1M tokens | $0.5/1M tokens |
| Output price | $0.96/1M tokens | $2/1M tokens |
| Providers |
Capabilities
| Mistral Large | o4-mini | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Mistral Large | o4-mini |
|---|---|---|
| MMLU PRO | 51.5 | 83.2 |
Deep dive
On shared benchmark coverage, MMLU PRO has Mistral Large at 51.5 and o4-mini at 83.2, with o4-mini ahead by 31.7 points. The largest visible gap is 31.7 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 multimodal input: o4-mini, reasoning mode: o4-mini, and code execution: o4-mini. Both models share vision, function calling, tool use, and 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, while o4-mini lists $0.5/1M input and $2/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Mistral Large lower by about $0.44 per million blended tokens. Availability is 8 providers versus 4, so concentration risk also matters.
Choose Mistral Large when vision-heavy evaluation, lower input-token cost, and broader provider choice are central to the workload. Choose o4-mini when coding workflow support are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which is cheaper, Mistral Large or o4-mini?
Mistral Large is cheaper on tracked token pricing. Mistral Large costs $0.32/1M input and $0.96/1M output tokens. o4-mini costs $0.5/1M input and $2/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Mistral Large or o4-mini open source?
Mistral Large is listed under Proprietary. o4-mini is listed under Proprietary. 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 o4-mini?
Both Mistral Large and o4-mini expose vision. 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 multimodal input, Mistral Large or o4-mini?
o4-mini 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 reasoning mode, Mistral Large or o4-mini?
o4-mini 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.
Where can I run Mistral Large and o4-mini?
Mistral Large is available on NVIDIA NIM, Microsoft Foundry, AWS Bedrock, Mistral AI Studio, and IBM watsonx. o4-mini is available on OpenAI API, OpenRouter, OpenAI Batch API, and Replicate API. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.