Llama 4 Scout 17B vs ShieldGemma 9B
Llama 4 Scout 17B (2025) and ShieldGemma 9B (2024) are compact production models from AI at Meta and Google DeepMind. Llama 4 Scout 17B ships a 10M-token context window, while ShieldGemma 9B ships a 8K-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 4 Scout 17B fits 1250x more tokens; pick it for long-context work and ShieldGemma 9B for tighter calls.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-10-01 | 2024-07-01 |
| Context window | 10M | 8K |
| Parameters | 17 | 9B |
| Architecture | - | decoder only |
| License | Open Source | 1 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Llama 4 Scout 17B | ShieldGemma 9B |
|---|---|---|
| Input price | $0.17/1M tokens | - |
| Output price | $0.66/1M tokens | - |
| Providers |
Capabilities
| Capability | Llama 4 Scout 17B | ShieldGemma 9B |
|---|---|---|
| Vision | No | No |
| Multimodal | Yes | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | No |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on multimodal input: Llama 4 Scout 17B and structured outputs: Llama 4 Scout 17B. 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 4 Scout 17B has $0.17/1M input tokens and ShieldGemma 9B 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 4 Scout 17B when long-context analysis and larger context windows are central to the workload. Choose ShieldGemma 9B 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 4 Scout 17B or ShieldGemma 9B?
Llama 4 Scout 17B supports 10M tokens, while ShieldGemma 9B supports 8K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Llama 4 Scout 17B or ShieldGemma 9B open source?
Llama 4 Scout 17B is listed under Open Source. ShieldGemma 9B 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 multimodal input, Llama 4 Scout 17B or ShieldGemma 9B?
Llama 4 Scout 17B has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for structured outputs, Llama 4 Scout 17B or ShieldGemma 9B?
Llama 4 Scout 17B 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 4 Scout 17B and ShieldGemma 9B?
Llama 4 Scout 17B is available on AWS Bedrock. ShieldGemma 9B is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
When should I pick Llama 4 Scout 17B over ShieldGemma 9B?
Llama 4 Scout 17B fits 1250x more tokens; pick it for long-context work and ShieldGemma 9B for tighter calls. If your workload also depends on long-context analysis, start with Llama 4 Scout 17B; if it depends on provider fit, run the same evaluation with ShieldGemma 9B.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.