Llama 3.3 Nemotron Super 49B v1 vs Mistral Medium 3
Llama 3.3 Nemotron Super 49B v1 (2025) and Mistral Medium 3 (2025) are compact production models from NVIDIA AI and MistralAI. Both ship 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. It focuses on practical selection signals rather than broad model-family marketing.
Llama 3.3 Nemotron Super 49B v1 is safer overall; choose Mistral Medium 3 when coding workflow support matters.
Decision scorecard
Local evidence first| Signal | Llama 3.3 Nemotron Super 49B v1 | Mistral Medium 3 |
|---|---|---|
| Best for | general production evaluation | multimodal apps, tool-calling agents, and provider-routed production |
| Decision fit | Long context | Coding, RAG, and Agents |
| Context window | 128K | 128K |
| Cheapest output | - | $2/1M tokens |
| Provider routes | 1 tracked | 5 tracked |
| Shared benchmarks | 0 shared | 0 shared |
Decision tradeoffs
- Local decision data tags Llama 3.3 Nemotron Super 49B v1 for Long context.
- Mistral Medium 3 has broader tracked provider coverage for fallback and route flexibility.
- Mistral Medium 3 uniquely exposes Vision, Multimodal, and JSON / Tool use in local model data.
- Local decision data tags Mistral Medium 3 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 Medium 3
$820
Cheapest tracked route/tier: Mistral AI Studio
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 Medium 3 adds Vision, Multimodal, and JSON / Tool use in local capability data.
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
- Check replacement coverage for Vision, Multimodal, and JSON / Tool use before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-06-01 | 2025-05-01 |
| Context window | 128K | 128K |
| Parameters | 49B | — |
| Architecture | Decoder Only | Decoder Only |
| License | NVIDIA Open Model | Proprietary |
| Openness | Open weights | Proprietary |
| Weights | Unknown | Not released |
| Code | Unknown | Unknown |
| Commercial use | Permitted | Restricted |
| Knowledge cutoff | - | 2025-03 |
Pricing and availability
| Pricing attribute | Llama 3.3 Nemotron Super 49B v1 | Mistral Medium 3 |
|---|---|---|
| Input price | - | $0.40/1M tokens |
| Output price | - | $2/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3.3 Nemotron Super 49B v1 | Mistral Medium 3 |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | No | No |
| JSON / Tool use | No | Yes |
| Structured outputs | No | Yes |
| Code execution | No | Yes |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark scores are currently available for this pair.
Deep dive
The capability footprint differs most on vision: Mistral Medium 3, multimodal input: Mistral Medium 3, JSON / Tool use: Mistral Medium 3, JSON / Tool use: Mistral Medium 3, structured outputs: Mistral Medium 3, and code execution: Mistral Medium 3. 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 Medium 3 has $0.40/1M 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 Medium 3 when coding workflow support 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.
FAQ
Which has a larger context window, Llama 3.3 Nemotron Super 49B v1 or Mistral Medium 3?
Llama 3.3 Nemotron Super 49B v1 supports 128K tokens, while Mistral Medium 3 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 Medium 3 open source?
Llama 3.3 Nemotron Super 49B v1 is listed under NVIDIA Open Model. Mistral Medium 3 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 vision, Llama 3.3 Nemotron Super 49B v1 or Mistral Medium 3?
Mistral Medium 3 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 Nemotron Super 49B v1 or Mistral Medium 3?
Mistral Medium 3 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 JSON / Tool use, Llama 3.3 Nemotron Super 49B v1 or Mistral Medium 3?
Mistral Medium 3 has the clearer documented JSON / Tool use signal in this comparison. If JSON / 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 Nemotron Super 49B v1 and Mistral Medium 3?
Llama 3.3 Nemotron Super 49B v1 is available on NVIDIA NIM. Mistral Medium 3 is available on NVIDIA NIM, Mistral AI Studio, Microsoft Foundry, OpenRouter, and GCP Vertex AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-10-18. Data sourced from public model cards and provider documentation.