Llama 3.3 Nemotron Super 49B v1 vs Magistral Small 2506
Llama 3.3 Nemotron Super 49B v1 (2025) and Magistral Small 2506 (2025) are frontier reasoning models from NVIDIA AI and MistralAI. Llama 3.3 Nemotron Super 49B v1 ships a 128k-token context window, while Magistral Small 2506 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.
Magistral Small 2506 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 | Magistral Small 2506 |
|---|---|---|
| Best for | general production evaluation | reasoning-heavy apps |
| Decision fit | Long context | Long context |
| Context window | 128k | 128k |
| Cheapest output | - | - |
| Provider routes | 1 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Local decision data tags Llama 3.3 Nemotron Super 49B v1 for Long context.
- Magistral Small 2506 uniquely exposes Reasoning in local model data.
- Local decision data tags Magistral Small 2506 for Long context.
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
Magistral Small 2506
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.
- Magistral Small 2506 adds Reasoning in local capability data.
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
- Check replacement coverage for Reasoning before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-06-01 | 2025-06-10 |
| Context window | 128k | 128k |
| Parameters | 49B | 24B |
| Architecture | decoder only | decoder only |
| License | 1 | Proprietary |
| Knowledge cutoff | - | 2025-06 |
Pricing and availability
| Pricing attribute | Llama 3.3 Nemotron Super 49B v1 | Magistral Small 2506 |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers |
Pricing not yet sourced for either model.
Capabilities
| Capability | Llama 3.3 Nemotron Super 49B v1 | Magistral Small 2506 |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | Yes |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | 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 reasoning mode: Magistral Small 2506. 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 Magistral Small 2506 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 Nemotron Super 49B v1 when provider fit are central to the workload. Choose Magistral Small 2506 when reasoning depth 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 3.3 Nemotron Super 49B v1 or Magistral Small 2506?
Llama 3.3 Nemotron Super 49B v1 supports 128k tokens, while Magistral Small 2506 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 Magistral Small 2506 open source?
Llama 3.3 Nemotron Super 49B v1 is listed under 1. Magistral Small 2506 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 reasoning mode, Llama 3.3 Nemotron Super 49B v1 or Magistral Small 2506?
Magistral Small 2506 has the clearer documented reasoning mode signal in this comparison. If reasoning mode 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 Magistral Small 2506?
Llama 3.3 Nemotron Super 49B v1 is available on NVIDIA NIM. Magistral Small 2506 is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.3 Nemotron Super 49B v1 over Magistral Small 2506?
Magistral Small 2506 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 reasoning depth, run the same evaluation with Magistral Small 2506.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.