Llama Guard 7B vs Nemotron 4 340B
Llama Guard 7B (2023) and Nemotron 4 340B (2025) are compact production models from AI at Meta and NVIDIA AI. Llama Guard 7B ships a 2k-token context window, while Nemotron 4 340B ships a 4k-token context window. On pricing, Llama Guard 7B costs $0.20/1M input tokens versus $4.20/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Llama Guard 7B is ~2000% cheaper at $0.20/1M; pay for Nemotron 4 340B only for long-context analysis.
Decision scorecard
Local evidence first| Signal | Llama Guard 7B | Nemotron 4 340B |
|---|---|---|
| Best for | provider-routed production | provider-routed production |
| Decision fit | Classification and JSON / Tool use | Classification and JSON / Tool use |
| Context window | 2k | 4k |
| Cheapest output | $0.20/1M tokens | $4.20/1M tokens |
| Provider routes | 2 tracked | 2 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama Guard 7B has the lower cheapest tracked output price at $0.20/1M tokens.
- Local decision data tags Llama Guard 7B for Classification and JSON / Tool use.
- Nemotron 4 340B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Nemotron 4 340B for Classification and JSON / Tool use.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama Guard 7B
$210
Cheapest tracked route/tier: Together AI
Nemotron 4 340B
$4,410
Cheapest tracked route/tier: DeepInfra
Estimated monthly gap: $4,200. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- No overlapping tracked provider route is sourced for Llama Guard 7B and Nemotron 4 340B; plan for SDK, billing, or endpoint changes.
- Nemotron 4 340B is $4/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- No overlapping tracked provider route is sourced for Nemotron 4 340B and Llama Guard 7B; plan for SDK, billing, or endpoint changes.
- Llama Guard 7B is $4/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
Specs
| Specification | ||
|---|---|---|
| Released | 2023-12-07 | 2025-02-27 |
| Context window | 2k | 4k |
| Parameters | 7B | 340B |
| Architecture | decoder only | decoder only |
| License | Llama 2 Community | NVIDIA Open Model |
| Openness | Open weights | Open weights |
| Commercial use | Commercial use with conditions | Commercial use allowed |
| Knowledge cutoff | 2022-09 | - |
Pricing and availability
| Pricing attribute | Llama Guard 7B | Nemotron 4 340B |
|---|---|---|
| Input price | $0.20/1M tokens | $4.20/1M tokens |
| Output price | $0.20/1M tokens | $4.20/1M tokens |
| Providers |
Capabilities
| Capability | Llama Guard 7B | Nemotron 4 340B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | 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 is close: both models cover structured outputs. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.
For cost, Llama Guard 7B lists $0.20/1M input and $0.20/1M output tokens on the cheapest tracked provider, while Nemotron 4 340B lists $4.20/1M input and $4.20/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama Guard 7B lower by about $4 per million blended tokens. Availability is 2 providers versus 2, so concentration risk also matters.
Choose Llama Guard 7B when provider fit and lower input-token cost are central to the workload. Choose Nemotron 4 340B when long-context analysis 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.
FAQ
Which has a larger context window, Llama Guard 7B or Nemotron 4 340B?
Nemotron 4 340B 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.
Which is cheaper, Llama Guard 7B or Nemotron 4 340B?
Llama Guard 7B is cheaper on tracked token pricing. Llama Guard 7B costs $0.20/1M input and $0.20/1M output tokens. Nemotron 4 340B costs $4.20/1M input and $4.20/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama Guard 7B or Nemotron 4 340B open source?
Llama Guard 7B is listed under Llama 2 Community. Nemotron 4 340B is listed under NVIDIA Open Model. 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 Guard 7B or Nemotron 4 340B?
Both Llama Guard 7B and Nemotron 4 340B expose structured outputs. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Where can I run Llama Guard 7B and Nemotron 4 340B?
Llama Guard 7B is available on Together AI and Fireworks AI. Nemotron 4 340B is available on NVIDIA NIM and DeepInfra. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama Guard 7B over Nemotron 4 340B?
Llama Guard 7B is ~2000% cheaper at $0.20/1M; pay for Nemotron 4 340B only for long-context analysis. If your workload also depends on provider fit, start with Llama Guard 7B; if it depends on long-context analysis, run the same evaluation with Nemotron 4 340B.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.