Llama 3.3 Nemotron Super 49B v1 vs Mistral Large 3 675B Instruct
Llama 3.3 Nemotron Super 49B v1 (2025) and Mistral Large 3 675B Instruct (2025) are compact production models from NVIDIA AI and MistralAI. Llama 3.3 Nemotron Super 49B v1 ships a 128k-token context window, while Mistral Large 3 675B Instruct ships a 128k-token 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 Large 3 675B Instruct is safer overall; choose Llama 3.3 Nemotron Super 49B v1 when provider fit matters.
Decision scorecard
Local evidence first| Signal | Llama 3.3 Nemotron Super 49B v1 | Mistral Large 3 675B Instruct |
|---|---|---|
| Best for | general production evaluation | provider-routed production |
| Decision fit | Long context | Coding, RAG, and Agents |
| Context window | 128k | 128k |
| Cheapest output | - | $1.50/1M tokens |
| Provider routes | 1 tracked | 5 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Local decision data tags Llama 3.3 Nemotron Super 49B v1 for Long context.
- Mistral Large 3 675B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Mistral Large 3 675B Instruct uniquely exposes Structured outputs in local model data.
- Local decision data tags Mistral Large 3 675B Instruct for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama 3.3 Nemotron Super 49B v1
Unavailable
No complete token price in local provider data
Mistral Large 3 675B Instruct
$775
Cheapest tracked route/tier: AWS Bedrock
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.
- Mistral Large 3 675B Instruct adds Structured outputs in local capability data.
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
- Check replacement coverage for Structured outputs before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-06-01 | 2025-12-01 |
| Context window | 128k | 128k |
| Parameters | 49B | 675B |
| Architecture | decoder only | decoder only |
| License | 1 | 1 |
| Knowledge cutoff | - | 2024-11 |
Pricing and availability
| Pricing attribute | Llama 3.3 Nemotron Super 49B v1 | Mistral Large 3 675B Instruct |
|---|---|---|
| Input price | - | $0.50/1M tokens |
| Output price | - | $1.50/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3.3 Nemotron Super 49B v1 | Mistral Large 3 675B Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | Yes |
| 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: Mistral Large 3 675B 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 3.3 Nemotron Super 49B v1 has no token price sourced yet and Mistral Large 3 675B Instruct has $0.50/1M input tokens. Provider availability is 1 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 3.3 Nemotron Super 49B v1 when provider fit are central to the workload. Choose Mistral Large 3 675B Instruct 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.
FAQ
Which has a larger context window, Llama 3.3 Nemotron Super 49B v1 or Mistral Large 3 675B Instruct?
Llama 3.3 Nemotron Super 49B v1 supports 128k tokens, while Mistral Large 3 675B Instruct 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 3.3 Nemotron Super 49B v1 or Mistral Large 3 675B Instruct open source?
Llama 3.3 Nemotron Super 49B v1 is listed under 1. Mistral Large 3 675B Instruct 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 structured outputs, Llama 3.3 Nemotron Super 49B v1 or Mistral Large 3 675B Instruct?
Mistral Large 3 675B 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 3.3 Nemotron Super 49B v1 and Mistral Large 3 675B Instruct?
Llama 3.3 Nemotron Super 49B v1 is available on NVIDIA NIM. Mistral Large 3 675B Instruct is available on AWS Bedrock, NVIDIA NIM, Mistral AI Studio, Microsoft Foundry, and Vercel AI Gateway. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.3 Nemotron Super 49B v1 over Mistral Large 3 675B Instruct?
Mistral Large 3 675B Instruct is safer overall; choose Llama 3.3 Nemotron Super 49B v1 when provider fit matters. If your workload also depends on provider fit, start with Llama 3.3 Nemotron Super 49B v1; if it depends on provider fit, run the same evaluation with Mistral Large 3 675B Instruct.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.