Llama Guard 7B vs ShieldGemma 9B
Llama Guard 7B (2023) and ShieldGemma 9B (2024) are compact production models from AI at Meta and Google DeepMind. Llama Guard 7B ships a 2k-token context window, while ShieldGemma 9B ships a 8k-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. It focuses on practical selection signals rather than broad model-family marketing.
ShieldGemma 9B fits 4x more tokens; pick it for long-context work and Llama Guard 7B for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama Guard 7B | ShieldGemma 9B |
|---|---|---|
| Best for | provider-routed production | general production evaluation |
| Decision fit | Classification and JSON / Tool use | Classification |
| Context window | 2k | 8k |
| Cheapest output | $0.20/1M tokens | - |
| Provider routes | 2 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- 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.
- ShieldGemma 9B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags ShieldGemma 9B for Classification.
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
ShieldGemma 9B
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Llama Guard 7B and ShieldGemma 9B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Structured outputs before moving production traffic.
- No overlapping tracked provider route is sourced for ShieldGemma 9B and Llama Guard 7B; plan for SDK, billing, or endpoint changes.
- Llama Guard 7B adds Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2023-12-07 | 2024-07-01 |
| Context window | 2k | 8k |
| Parameters | 7B | 9B |
| Architecture | decoder only | decoder only |
| License | Llama 2 Community | Gemma |
| Openness | Open weights | Open weights |
| Commercial use | Commercial use with conditions | Commercial use with conditions |
| Knowledge cutoff | 2022-09 | - |
Pricing and availability
| Pricing attribute | Llama Guard 7B | ShieldGemma 9B |
|---|---|---|
| Input price | $0.20/1M tokens | - |
| Output price | $0.20/1M tokens | - |
| Providers |
Capabilities
| Capability | Llama Guard 7B | ShieldGemma 9B |
|---|---|---|
| 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 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 Guard 7B has $0.20/1M input tokens and ShieldGemma 9B has no token price sourced yet. Provider availability is 2 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 7B when provider fit and broader provider choice are central to the workload. Choose ShieldGemma 9B 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. 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 7B or ShieldGemma 9B?
ShieldGemma 9B supports 8k 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 Guard 7B or ShieldGemma 9B open source?
Llama Guard 7B is listed under Llama 2 Community. ShieldGemma 9B is listed under Gemma. 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 ShieldGemma 9B?
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 Guard 7B and ShieldGemma 9B?
Llama Guard 7B is available on Together AI and Fireworks AI. ShieldGemma 9B is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama Guard 7B over ShieldGemma 9B?
ShieldGemma 9B fits 4x more tokens; pick it for long-context work and Llama Guard 7B for tighter calls. 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 ShieldGemma 9B.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.