Llama 3.2 1B vs ShieldGemma 9B
Llama 3.2 1B (2024) and ShieldGemma 9B (2024) are compact production models from AI at Meta and Google DeepMind. Llama 3.2 1B ships a 128k-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.
Llama 3.2 1B fits 16x more tokens; pick it for long-context work and ShieldGemma 9B for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama 3.2 1B | ShieldGemma 9B |
|---|---|---|
| Best for | general production evaluation | general production evaluation |
| Decision fit | Coding, Long context, and Classification | Classification |
| Context window | 128k | 8k |
| Cheapest output | $0.10/1M tokens | - |
| Provider routes | 1 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 3.2 1B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Llama 3.2 1B for Coding, Long context, and Classification.
- 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 3.2 1B
$105
Cheapest tracked route/tier: Fireworks 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 3.2 1B and ShieldGemma 9B; plan for SDK, billing, or endpoint changes.
- No overlapping tracked provider route is sourced for ShieldGemma 9B and Llama 3.2 1B; plan for SDK, billing, or endpoint changes.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-09-25 | 2024-07-01 |
| Context window | 128k | 8k |
| Parameters | 1.23B | 9B |
| Architecture | decoder only | decoder only |
| License | Llama 3 Community | Gemma |
| Openness | Open weights | Open weights |
| Commercial use | Commercial use with conditions | Commercial use with conditions |
| Knowledge cutoff | 2023-12 | - |
Pricing and availability
| Pricing attribute | Llama 3.2 1B | ShieldGemma 9B |
|---|---|---|
| Input price | $0.10/1M tokens | - |
| Output price | $0.10/1M tokens | - |
| Providers |
Capabilities
| Capability | Llama 3.2 1B | ShieldGemma 9B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | 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 is close: both models cover the core production surface. 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.
Pricing coverage is uneven: Llama 3.2 1B has $0.10/1M 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 3.2 1B 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 3.2 1B or ShieldGemma 9B?
Llama 3.2 1B supports 128k 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 3.2 1B or ShieldGemma 9B open source?
Llama 3.2 1B is listed under Llama 3 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.
Where can I run Llama 3.2 1B and ShieldGemma 9B?
Llama 3.2 1B is available on Fireworks AI. 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 3.2 1B over ShieldGemma 9B?
Llama 3.2 1B fits 16x 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 3.2 1B; if it depends on provider fit, run the same evaluation with ShieldGemma 9B.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.