Llama 3.1 405B vs Mistral NeMo (2407)
Llama 3.1 405B (2024) and Mistral NeMo (2407) (2024) are compact production models from AI at Meta and MistralAI. Llama 3.1 405B ships a 128K-token context window, while Mistral NeMo (2407) ships a 128K-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing. The goal is to make the tradeoff clear before deeper testing.
Llama 3.1 405B is safer overall; choose Mistral NeMo (2407) when provider fit matters.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-07-23 | 2024-07-18 |
| Context window | 128K | 128K |
| Parameters | 405B | 12B |
| Architecture | decoder only | decoder only |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Llama 3.1 405B | Mistral NeMo (2407) |
|---|---|---|
| Input price | - | $0.02/1M tokens |
| Output price | - | $0.03/1M tokens |
| Providers | - |
Capabilities
| Capability | Llama 3.1 405B | Mistral NeMo (2407) |
|---|---|---|
| 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 |
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 405B has no token price sourced yet and Mistral NeMo (2407) has $0.02/1M input tokens. Provider availability is 0 tracked routes versus 5. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Llama 3.1 405B when provider fit are central to the workload. Choose Mistral NeMo (2407) 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
Which has a larger context window, Llama 3.1 405B or Mistral NeMo (2407)?
Llama 3.1 405B supports 128K tokens, while Mistral NeMo (2407) supports 128K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Llama 3.1 405B or Mistral NeMo (2407) open source?
Llama 3.1 405B is listed under Open Source. Mistral NeMo (2407) is listed under Apache 2.0. 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 405B and Mistral NeMo (2407)?
Llama 3.1 405B is available on the tracked providers still being sourced. Mistral NeMo (2407) is available on Mistral AI Studio, OpenRouter, Fireworks AI, Bitdeer AI, and SiliconFlow. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.1 405B over Mistral NeMo (2407)?
Llama 3.1 405B is safer overall; choose Mistral NeMo (2407) when provider fit matters. If your workload also depends on provider fit, start with Llama 3.1 405B; if it depends on provider fit, run the same evaluation with Mistral NeMo (2407).
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.