Llama 3.2 1B vs Mistral Magistral Small 2509
Llama 3.2 1B (2024) and Mistral Magistral Small 2509 (2025) are compact production models from AI at Meta and MistralAI. Llama 3.2 1B ships a 128K-token context window, while Mistral Magistral Small 2509 ships a not-yet-sourced context window. On pricing, Llama 3.2 1B costs $0.1/1M input tokens versus $0.5/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 3.2 1B is ~400% cheaper at $0.1/1M; pay for Mistral Magistral Small 2509 only for provider fit.
Specs
| Released | 2024-09-25 | 2025-09-01 |
| Context window | 128K | — |
| Parameters | 1.23B | — |
| Architecture | decoder only | - |
| License | Open Source | Proprietary |
| Knowledge cutoff | 2023-12 | - |
Pricing and availability
| Llama 3.2 1B | Mistral Magistral Small 2509 | |
|---|---|---|
| Input price | $0.1/1M tokens | $0.5/1M tokens |
| Output price | $0.1/1M tokens | $1.5/1M tokens |
| Providers |
Capabilities
| Llama 3.2 1B | 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 is close: both models cover the core production surface. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.
For cost, Llama 3.2 1B lists $0.1/1M input and $0.1/1M 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 3.2 1B lower by about $0.7 per million blended tokens. Availability is 1 providers versus 1, so concentration risk also matters.
Choose Llama 3.2 1B when provider fit and lower input-token cost 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. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which is cheaper, Llama 3.2 1B or Mistral Magistral Small 2509?
Llama 3.2 1B is cheaper on tracked token pricing. Llama 3.2 1B costs $0.1/1M input and $0.1/1M 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 3.2 1B or Mistral Magistral Small 2509 open source?
Llama 3.2 1B 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.
Where can I run Llama 3.2 1B and Mistral Magistral Small 2509?
Llama 3.2 1B is available on Fireworks AI. Mistral Magistral Small 2509 is available on AWS Bedrock. 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 Llama 3.2 1B over Mistral Magistral Small 2509?
Llama 3.2 1B is ~400% cheaper at $0.1/1M; pay for Mistral Magistral Small 2509 only for provider fit. If your workload also depends on provider fit, start with Llama 3.2 1B; if it depends on provider fit, run the same evaluation with Mistral Magistral Small 2509.
Continue comparing
Last reviewed: 2026-04-19. Data sourced from public model cards and provider documentation.