Llama 3.1 Nemotron 70B Reward vs Mistral Magistral Small 2509
Llama 3.1 Nemotron 70B Reward (2024) and Mistral Magistral Small 2509 (2025) are compact production models from NVIDIA AI and MistralAI. Llama 3.1 Nemotron 70B Reward ships a 4k-token context window, while Mistral Magistral Small 2509 ships a not-yet-sourced context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Mistral Magistral Small 2509 is safer overall; choose Llama 3.1 Nemotron 70B Reward when provider fit matters.
Decision scorecard
Local evidence first| Signal | Llama 3.1 Nemotron 70B Reward | Mistral Magistral Small 2509 |
|---|---|---|
| Best for | general production evaluation | provider-routed production |
| Decision fit | Classification | General |
| Context window | 4k | — |
| Cheapest output | - | $1.50/1M tokens |
| Provider routes | 1 tracked | 2 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 3.1 Nemotron 70B Reward has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Llama 3.1 Nemotron 70B Reward for Classification.
- Mistral Magistral Small 2509 has broader tracked provider coverage for fallback and procurement flexibility.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama 3.1 Nemotron 70B Reward
Unavailable
No complete token price in local provider data
Mistral Magistral Small 2509
$775
Cheapest tracked route/tier: AWS Bedrock
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Llama 3.1 Nemotron 70B Reward and Mistral Magistral Small 2509; plan for SDK, billing, or endpoint changes.
- No overlapping tracked provider route is sourced for Mistral Magistral Small 2509 and Llama 3.1 Nemotron 70B Reward; plan for SDK, billing, or endpoint changes.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-10-01 | 2025-09-01 |
| Context window | 4k | — |
| Parameters | 70B | 24B |
| Architecture | decoder only | - |
| License | 1 | Proprietary |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Llama 3.1 Nemotron 70B Reward | Mistral Magistral Small 2509 |
|---|---|---|
| Input price | - | $0.50/1M tokens |
| Output price | - | $1.50/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3.1 Nemotron 70B Reward | Mistral Magistral Small 2509 |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | No |
| 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 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.
Pricing coverage is uneven: Llama 3.1 Nemotron 70B Reward has no token price sourced yet and Mistral Magistral Small 2509 has $0.50/1M input tokens. Provider availability is 1 tracked routes versus 2. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Llama 3.1 Nemotron 70B Reward when provider fit are central to the workload. Choose Mistral Magistral Small 2509 when provider fit and broader provider choice 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
Is Llama 3.1 Nemotron 70B Reward or Mistral Magistral Small 2509 open source?
Llama 3.1 Nemotron 70B Reward is listed under 1. 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 3.1 Nemotron 70B Reward and Mistral Magistral Small 2509?
Llama 3.1 Nemotron 70B Reward is available on NVIDIA NIM. Mistral Magistral Small 2509 is available on AWS Bedrock and Vercel AI Gateway. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.1 Nemotron 70B Reward over Mistral Magistral Small 2509?
Mistral Magistral Small 2509 is safer overall; choose Llama 3.1 Nemotron 70B Reward when provider fit matters. If your workload also depends on provider fit, start with Llama 3.1 Nemotron 70B Reward; if it depends on provider fit, run the same evaluation with Mistral Magistral Small 2509.
What is the main difference between Llama 3.1 Nemotron 70B Reward and Mistral Magistral Small 2509?
Llama 3.1 Nemotron 70B Reward and Mistral Magistral Small 2509 differ most on context, provider coverage, capabilities, or pricing depending on the data currently sourced. Use the specs table first, then validate the model behavior with your own prompts.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.