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Mistral Magistral Small 2509 vs o1-pro

Mistral Magistral Small 2509 (2025) and o1-pro (2024) are general-purpose language models from MistralAI and OpenAI. Mistral Magistral Small 2509 ships a not-yet-sourced context window, while o1-pro ships a 200K-token context window. On pricing, Mistral Magistral Small 2509 costs $0.5/1M input tokens versus $150/1M 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.

Mistral Magistral Small 2509 is ~29900% cheaper at $0.5/1M; pay for o1-pro only for provider fit.

Specs

Specification
Released2025-09-012024-12-05
Context window200K
Parameters
Architecture-decoder only
LicenseProprietaryUnknown
Knowledge cutoff--

Pricing and availability

Pricing attributeMistral Magistral Small 2509o1-pro
Input price$0.5/1M tokens$150/1M tokens
Output price$1.5/1M tokens$600/1M tokens
Providers

Capabilities

CapabilityMistral Magistral Small 2509o1-pro
VisionNoNo
MultimodalNoNo
ReasoningNoNo
Function callingNoNo
Tool useNoNo
Structured outputsNoYes
Code executionNoNo

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on structured outputs: o1-pro. 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.

For cost, Mistral Magistral Small 2509 lists $0.5/1M input and $1.5/1M output tokens, while o1-pro lists $150/1M input and $600/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Mistral Magistral Small 2509 lower by about $284 per million blended tokens. Availability is 1 providers versus 1, so concentration risk also matters.

Choose Mistral Magistral Small 2509 when provider fit and lower input-token cost are central to the workload. Choose o1-pro 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. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.

FAQ

Which is cheaper, Mistral Magistral Small 2509 or o1-pro?

Mistral Magistral Small 2509 is cheaper on tracked token pricing. Mistral Magistral Small 2509 costs $0.5/1M input and $1.5/1M output tokens. o1-pro costs $150/1M input and $600/1M output tokens. Provider discounts or batch pricing can still change the final bill.

Is Mistral Magistral Small 2509 or o1-pro open source?

Mistral Magistral Small 2509 is listed under Proprietary. o1-pro is listed under Unknown. 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 structured outputs, Mistral Magistral Small 2509 or o1-pro?

o1-pro has the clearer documented structured outputs signal in this comparison. If structured outputs is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Where can I run Mistral Magistral Small 2509 and o1-pro?

Mistral Magistral Small 2509 is available on AWS Bedrock. o1-pro is available on OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.

When should I pick Mistral Magistral Small 2509 over o1-pro?

Mistral Magistral Small 2509 is ~29900% cheaper at $0.5/1M; pay for o1-pro only for provider fit. If your workload also depends on provider fit, start with Mistral Magistral Small 2509; if it depends on provider fit, run the same evaluation with o1-pro.

Continue comparing

Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.