Llama 3.3 70B vs Mistral Nemotron
Llama 3.3 70B (2025) and Mistral Nemotron (2025) are compact production models from AI at Meta and MistralAI. Llama 3.3 70B ships a 8K-token context window, while Mistral Nemotron ships a not-yet-sourced 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 3.3 70B is safer overall; choose Mistral Nemotron when provider fit matters.
Specs
| Released | 2025-12-09 | 2025-12-01 |
| Context window | 8K | — |
| Parameters | 70B | — |
| Architecture | decoder only | decoder only |
| License | True | 1 |
| Knowledge cutoff | 2024-12 | - |
Pricing and availability
| Llama 3.3 70B | Mistral Nemotron | |
|---|---|---|
| Input price | $0.9/1M tokens | - |
| Output price | $0.9/1M tokens | - |
| Providers |
Capabilities
| Llama 3.3 70B | Mistral Nemotron | |
|---|---|---|
| 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 vision: Llama 3.3 70B, multimodal input: Llama 3.3 70B, function calling: Llama 3.3 70B, and tool use: Llama 3.3 70B. 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 3.3 70B has $0.9/1M input tokens and Mistral Nemotron has no token price sourced yet. Provider availability is 1 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 3.3 70B when vision-heavy evaluation 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 3.3 70B or Mistral Nemotron open source?
Llama 3.3 70B is listed under True. Mistral Nemotron is listed under 1. 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 vision, Llama 3.3 70B or Mistral Nemotron?
Llama 3.3 70B has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for multimodal input, Llama 3.3 70B or Mistral Nemotron?
Llama 3.3 70B has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for function calling, Llama 3.3 70B or Mistral Nemotron?
Llama 3.3 70B has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for tool use, Llama 3.3 70B or Mistral Nemotron?
Llama 3.3 70B has the clearer documented tool use signal in this comparison. If tool use is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Llama 3.3 70B and Mistral Nemotron?
Llama 3.3 70B is available on Fireworks AI. Mistral Nemotron is available on NVIDIA NIM. 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.
Continue comparing
Last reviewed: 2026-04-19. Data sourced from public model cards and provider documentation.