Llama 3.1 70B Instruct vs Mistral Nemotron
Llama 3.1 70B Instruct (2024) and Mistral Nemotron (2025) are compact production models from AI at Meta and MistralAI. Llama 3.1 70B Instruct ships a 128k-token context window, while Mistral Nemotron 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. It focuses on practical selection signals rather than broad model-family marketing.
Mistral Nemotron is safer overall; choose Llama 3.1 70B Instruct when provider fit matters.
Decision scorecard
Local evidence first| Signal | Llama 3.1 70B Instruct | Mistral Nemotron |
|---|---|---|
| Best for | provider-routed production | general production evaluation |
| Decision fit | Coding, RAG, and Long context | General |
| Context window | 128k | — |
| Cheapest output | $0.40/1M tokens | - |
| Provider routes | 13 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 3.1 70B Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 3.1 70B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Llama 3.1 70B Instruct uniquely exposes Structured outputs in local model data.
- Local decision data tags Llama 3.1 70B Instruct for Coding, RAG, and Long context.
- Use Mistral Nemotron when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama 3.1 70B Instruct
$420
Cheapest tracked route/tier: Hyperbolic AI Inference
Mistral Nemotron
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
- Check replacement coverage for Structured outputs before moving production traffic.
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
- Llama 3.1 70B Instruct adds Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-07-23 | 2025-12-01 |
| Context window | 128k | — |
| Parameters | 70B | 70B |
| Architecture | decoder only | decoder only |
| License | Llama 3 Community | Proprietary |
| Openness | Open weights | Proprietary |
| Commercial use | Commercial use with conditions | - |
| Knowledge cutoff | 2023-12 | - |
Pricing and availability
| Pricing attribute | Llama 3.1 70B Instruct | Mistral Nemotron |
|---|---|---|
| Input price | $0.40/1M tokens | - |
| Output price | $0.40/1M tokens | - |
| Providers |
Capabilities
| Capability | Llama 3.1 70B Instruct | Mistral Nemotron |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | 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 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.
Pricing coverage is uneven: Llama 3.1 70B Instruct has $0.40/1M input tokens and Mistral Nemotron has no token price sourced yet. Provider availability is 13 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Llama 3.1 70B Instruct when provider fit and broader provider choice are central to the workload. Choose Mistral Nemotron 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
Is Llama 3.1 70B Instruct or Mistral Nemotron open source?
Llama 3.1 70B Instruct is listed under Llama 3 Community. Mistral Nemotron 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 Nemotron?
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 Nemotron?
Llama 3.1 70B Instruct is available on Cloudflare Workers AI, OctoAI API (Deprecated), Together AI, Fireworks AI, and NVIDIA NIM. Mistral Nemotron is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.1 70B Instruct over Mistral Nemotron?
Mistral Nemotron 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 Nemotron.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.