Llama Guard 4 12B vs NV-EmbedCode 7B v1
Llama Guard 4 12B (2025) and NV-EmbedCode 7B v1 (2025) are compact production models from AI at Meta and NVIDIA AI. Llama Guard 4 12B ships a 164k-token context window, while NV-EmbedCode 7B v1 ships a 4k-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.
Llama Guard 4 12B fits 41x more tokens; pick it for long-context work and NV-EmbedCode 7B v1 for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama Guard 4 12B | NV-EmbedCode 7B v1 |
|---|---|---|
| Best for | provider-routed production | general production evaluation |
| Decision fit | RAG, Long context, and Classification | General |
| Context window | 164k | 4k |
| Cheapest output | $0.18/1M tokens | - |
| Provider routes | 3 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama Guard 4 12B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama Guard 4 12B has broader tracked provider coverage for fallback and procurement flexibility.
- Llama Guard 4 12B uniquely exposes Structured outputs in local model data.
- Local decision data tags Llama Guard 4 12B for RAG, Long context, and Classification.
- Use NV-EmbedCode 7B v1 when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama Guard 4 12B
$189
Cheapest tracked route/tier: OpenRouter
NV-EmbedCode 7B v1
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.
- Check replacement coverage for Structured outputs before moving production traffic.
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
- Llama Guard 4 12B adds Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-04-05 | 2025-06-01 |
| Context window | 164k | 4k |
| Parameters | 12B | 7B |
| Architecture | decoder only | encoder |
| License | Proprietary | 1 |
| Knowledge cutoff | 2024-08 | - |
Pricing and availability
| Pricing attribute | Llama Guard 4 12B | NV-EmbedCode 7B v1 |
|---|---|---|
| Input price | $0.18/1M tokens | - |
| Output price | $0.18/1M tokens | - |
| Providers |
Capabilities
| Capability | Llama Guard 4 12B | NV-EmbedCode 7B v1 |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | 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 structured outputs: Llama Guard 4 12B. 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 Guard 4 12B has $0.18/1M input tokens and NV-EmbedCode 7B v1 has no token price sourced yet. Provider availability is 3 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 Guard 4 12B when long-context analysis, larger context windows, and broader provider choice are central to the workload. Choose NV-EmbedCode 7B v1 when provider fit 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 Guard 4 12B or NV-EmbedCode 7B v1?
Llama Guard 4 12B supports 164k tokens, while NV-EmbedCode 7B v1 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 Guard 4 12B or NV-EmbedCode 7B v1 open source?
Llama Guard 4 12B is listed under Proprietary. NV-EmbedCode 7B v1 is listed under 1. 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 4 12B or NV-EmbedCode 7B v1?
Llama Guard 4 12B 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 Guard 4 12B and NV-EmbedCode 7B v1?
Llama Guard 4 12B is available on NVIDIA NIM, Replicate API, and OpenRouter. NV-EmbedCode 7B v1 is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama Guard 4 12B over NV-EmbedCode 7B v1?
Llama Guard 4 12B fits 41x more tokens; pick it for long-context work and NV-EmbedCode 7B v1 for tighter calls. If your workload also depends on long-context analysis, start with Llama Guard 4 12B; if it depends on provider fit, run the same evaluation with NV-EmbedCode 7B v1.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.