Llama 3.1 Nemotron Nano 8B v1 vs Llama Guard 7B
Llama 3.1 Nemotron Nano 8B v1 (2025) and Llama Guard 7B (2023) are compact production models from NVIDIA AI and AI at Meta. Llama 3.1 Nemotron Nano 8B v1 ships a 4K-token context window, while Llama Guard 7B ships a 2K-token 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.
Llama 3.1 Nemotron Nano 8B v1 is safer overall; choose Llama Guard 7B when provider fit matters.
Decision scorecard
Local evidence first| Signal | Llama 3.1 Nemotron Nano 8B v1 | Llama Guard 7B |
|---|---|---|
| Decision fit | General | Classification and JSON / Tool use |
| Context window | 4K | 2K |
| Cheapest output | - | $0.2/1M tokens |
| Provider routes | 1 tracked | 3 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 3.1 Nemotron Nano 8B v1 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama Guard 7B has broader tracked provider coverage for fallback and procurement flexibility.
- Llama Guard 7B uniquely exposes Structured outputs in local model data.
- Local decision data tags Llama Guard 7B for Classification and JSON / Tool use.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Llama 3.1 Nemotron Nano 8B v1
Unavailable
No complete token price in local provider data
Llama Guard 7B
$210
Cheapest tracked route: Together AI
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Llama 3.1 Nemotron Nano 8B v1 and Llama Guard 7B; plan for SDK, billing, or endpoint changes.
- Llama Guard 7B adds Structured outputs in local capability data.
- No overlapping tracked provider route is sourced for Llama Guard 7B and Llama 3.1 Nemotron Nano 8B v1; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Structured outputs before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-03-01 | 2023-12-07 |
| Context window | 4K | 2K |
| Parameters | 8B | 7B |
| Architecture | decoder only | decoder only |
| License | 1 | Open Source |
| Knowledge cutoff | - | 2022-09 |
Pricing and availability
| Pricing attribute | Llama 3.1 Nemotron Nano 8B v1 | Llama Guard 7B |
|---|---|---|
| Input price | - | $0.2/1M tokens |
| Output price | - | $0.2/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3.1 Nemotron Nano 8B v1 | Llama Guard 7B |
|---|---|---|
| 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 |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on structured outputs: Llama Guard 7B. 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 Nemotron Nano 8B v1 has no token price sourced yet and Llama Guard 7B has $0.2/1M input tokens. Provider availability is 1 tracked routes versus 3. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Llama 3.1 Nemotron Nano 8B v1 when long-context analysis and larger context windows are central to the workload. Choose Llama Guard 7B 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. 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 Nemotron Nano 8B v1 or Llama Guard 7B?
Llama 3.1 Nemotron Nano 8B v1 supports 4K tokens, while Llama Guard 7B supports 2K 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 Nemotron Nano 8B v1 or Llama Guard 7B open source?
Llama 3.1 Nemotron Nano 8B v1 is listed under 1. Llama Guard 7B is listed under Open Source. 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 3.1 Nemotron Nano 8B v1 or Llama Guard 7B?
Llama Guard 7B 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 3.1 Nemotron Nano 8B v1 and Llama Guard 7B?
Llama 3.1 Nemotron Nano 8B v1 is available on NVIDIA NIM. Llama Guard 7B is available on Cloudflare Workers AI, Together AI, and Fireworks AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.1 Nemotron Nano 8B v1 over Llama Guard 7B?
Llama 3.1 Nemotron Nano 8B v1 is safer overall; choose Llama Guard 7B when provider fit matters. If your workload also depends on long-context analysis, start with Llama 3.1 Nemotron Nano 8B v1; if it depends on provider fit, run the same evaluation with Llama Guard 7B.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.