Llama 3.1 70B Instruct vs Mistral Magistral Small 2509
Llama 3.1 70B Instruct (2024) and Mistral Magistral Small 2509 (2025) are compact production models from AI at Meta and MistralAI. Llama 3.1 70B Instruct ships a 128K-token context window, while Mistral Magistral Small 2509 ships a not-yet-sourced context window. On pricing, Llama 3.1 70B Instruct costs $0.4/1M input tokens versus $0.5/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Mistral Magistral Small 2509 is safer overall; choose Llama 3.1 70B Instruct when provider fit matters.
Specs
| Released | 2024-07-23 | 2025-09-01 |
| Context window | 128K | — |
| Parameters | 70B | — |
| Architecture | decoder only | - |
| License | Open Source | Proprietary |
| Knowledge cutoff | - | - |
Pricing and availability
| Llama 3.1 70B Instruct | Mistral Magistral Small 2509 | |
|---|---|---|
| Input price | $0.4/1M tokens | $0.5/1M tokens |
| Output price | $0.4/1M tokens | $1.5/1M tokens |
| Providers |
Capabilities
| Llama 3.1 70B Instruct | Mistral Magistral Small 2509 | |
|---|---|---|
| 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 structured outputs: Llama 3.1 70B Instruct. 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, Llama 3.1 70B Instruct lists $0.4/1M input and $0.4/1M output tokens, while Mistral Magistral Small 2509 lists $0.5/1M input and $1.5/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.1 70B Instruct lower by about $0.4 per million blended tokens. Availability is 11 providers versus 1, so concentration risk also matters.
Choose Llama 3.1 70B Instruct when provider fit, lower input-token cost, and broader provider choice are central to the workload. Choose Mistral Magistral Small 2509 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 is cheaper, Llama 3.1 70B Instruct or Mistral Magistral Small 2509?
Llama 3.1 70B Instruct is cheaper on tracked token pricing. Llama 3.1 70B Instruct costs $0.4/1M input and $0.4/1M output tokens. Mistral Magistral Small 2509 costs $0.5/1M input and $1.5/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 3.1 70B Instruct or Mistral Magistral Small 2509 open source?
Llama 3.1 70B Instruct is listed under Open Source. Mistral Magistral Small 2509 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 structured outputs, Llama 3.1 70B Instruct or Mistral Magistral Small 2509?
Llama 3.1 70B Instruct 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 Llama 3.1 70B Instruct and Mistral Magistral Small 2509?
Llama 3.1 70B Instruct is available on OctoAI API, Together AI, Fireworks AI, NVIDIA NIM, and Microsoft Foundry. Mistral Magistral Small 2509 is available on AWS Bedrock. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.1 70B Instruct over Mistral Magistral Small 2509?
Mistral Magistral Small 2509 is safer overall; choose Llama 3.1 70B Instruct when provider fit matters. If your workload also depends on provider fit, start with Llama 3.1 70B Instruct; if it depends on provider fit, run the same evaluation with Mistral Magistral Small 2509.
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Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.