Llama 3.2 NV EmbedQA 1B v1 vs ShieldGemma 2
Llama 3.2 NV EmbedQA 1B v1 (2024) and ShieldGemma 2 (2024) are compact production models from NVIDIA AI and Google DeepMind. Llama 3.2 NV EmbedQA 1B v1 ships a 512-token context window, while ShieldGemma 2 ships a not-yet-sourced 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 3.2 NV EmbedQA 1B v1 is safer overall; choose ShieldGemma 2 when vision-heavy evaluation matters.
Decision scorecard
Local evidence first| Signal | Llama 3.2 NV EmbedQA 1B v1 | ShieldGemma 2 |
|---|---|---|
| Decision fit | General | Agents, Vision, and Classification |
| Context window | 512 | — |
| Cheapest output | - | - |
| Provider routes | 1 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 3.2 NV EmbedQA 1B v1 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- ShieldGemma 2 uniquely exposes Vision, Multimodal, and Function calling in local model data.
- Local decision data tags ShieldGemma 2 for Agents, Vision, and Classification.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Llama 3.2 NV EmbedQA 1B v1
Unavailable
No complete token price in local provider data
ShieldGemma 2
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 NV EmbedQA 1B v1 and ShieldGemma 2; plan for SDK, billing, or endpoint changes.
- ShieldGemma 2 adds Vision, Multimodal, and Function calling in local capability data.
- No overlapping tracked provider route is sourced for ShieldGemma 2 and Llama 3.2 NV EmbedQA 1B v1; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Vision, Multimodal, and Function calling before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-10-08 | 2024-09-01 |
| Context window | 512 | — |
| Parameters | 1B | — |
| Architecture | encoder | decoder only |
| License | 1 | Proprietary |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Llama 3.2 NV EmbedQA 1B v1 | ShieldGemma 2 |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers |
Pricing not yet sourced for either model.
Capabilities
| Capability | Llama 3.2 NV EmbedQA 1B v1 | ShieldGemma 2 |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | No | No |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | No | Yes |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: ShieldGemma 2, multimodal input: ShieldGemma 2, function calling: ShieldGemma 2, tool use: ShieldGemma 2, and structured outputs: ShieldGemma 2. 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 3.2 NV EmbedQA 1B v1 has no token price sourced yet and ShieldGemma 2 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 NV EmbedQA 1B v1 when provider fit are central to the workload. Choose ShieldGemma 2 when vision-heavy evaluation 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
Is Llama 3.2 NV EmbedQA 1B v1 or ShieldGemma 2 open source?
Llama 3.2 NV EmbedQA 1B v1 is listed under 1. ShieldGemma 2 is listed under Proprietary. 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 vision, Llama 3.2 NV EmbedQA 1B v1 or ShieldGemma 2?
ShieldGemma 2 has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, Llama 3.2 NV EmbedQA 1B v1 or ShieldGemma 2?
ShieldGemma 2 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 function calling, Llama 3.2 NV EmbedQA 1B v1 or ShieldGemma 2?
ShieldGemma 2 has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for tool use, Llama 3.2 NV EmbedQA 1B v1 or ShieldGemma 2?
ShieldGemma 2 has the clearer documented tool use signal in this comparison. If tool use is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Llama 3.2 NV EmbedQA 1B v1 and ShieldGemma 2?
Llama 3.2 NV EmbedQA 1B v1 is available on NVIDIA NIM. ShieldGemma 2 is available on GCP Vertex AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.