Llama 3.1 NemoGuard 8B Content Safety vs Magistral Small 2506
Llama 3.1 NemoGuard 8B Content Safety (2025) and Magistral Small 2506 (2025) are frontier reasoning models from NVIDIA AI and MistralAI. Llama 3.1 NemoGuard 8B Content Safety ships a 4k-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 fits 32x more tokens; pick it for long-context work and Llama 3.1 NemoGuard 8B Content Safety for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama 3.1 NemoGuard 8B Content Safety | Magistral Small 2506 |
|---|---|---|
| Best for | general production evaluation | reasoning-heavy apps |
| Decision fit | Classification | Long context |
| Context window | 4k | 128k |
| Cheapest output | - | - |
| Provider routes | 1 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Local decision data tags Llama 3.1 NemoGuard 8B Content Safety for Classification.
- Magistral Small 2506 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- 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 3.1 NemoGuard 8B Content Safety
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 | 2025-01-01 | 2025-06-10 |
| Context window | 4k | 128k |
| Parameters | 8B | 24B |
| Architecture | decoder only | decoder only |
| License | 1 | Proprietary |
| Knowledge cutoff | - | 2025-06 |
Pricing and availability
| Pricing attribute | Llama 3.1 NemoGuard 8B Content Safety | Magistral Small 2506 |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers |
Pricing not yet sourced for either model.
Capabilities
| Capability | Llama 3.1 NemoGuard 8B Content Safety | 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 3.1 NemoGuard 8B Content Safety 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 3.1 NemoGuard 8B Content Safety when provider fit are central to the workload. Choose Magistral Small 2506 when reasoning depth and larger context windows 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 3.1 NemoGuard 8B Content Safety or Magistral Small 2506?
Magistral Small 2506 supports 128k tokens, while Llama 3.1 NemoGuard 8B Content Safety supports 4k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Llama 3.1 NemoGuard 8B Content Safety or Magistral Small 2506 open source?
Llama 3.1 NemoGuard 8B Content Safety 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 3.1 NemoGuard 8B Content Safety 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 3.1 NemoGuard 8B Content Safety and Magistral Small 2506?
Llama 3.1 NemoGuard 8B Content Safety 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 3.1 NemoGuard 8B Content Safety over Magistral Small 2506?
Magistral Small 2506 fits 32x more tokens; pick it for long-context work and Llama 3.1 NemoGuard 8B Content Safety for tighter calls. If your workload also depends on provider fit, start with Llama 3.1 NemoGuard 8B Content Safety; 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.