Llama 3.1 Nemotron Nano VL 8B v1 vs ShieldGemma 9B
Llama 3.1 Nemotron Nano VL 8B v1 (2025) and ShieldGemma 9B (2024) are compact production models from NVIDIA AI and Google DeepMind. Llama 3.1 Nemotron Nano VL 8B v1 ships a 4K-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 3.1 Nemotron Nano VL 8B v1 is safer overall; choose ShieldGemma 9B when long-context analysis matters.
Decision scorecard
Local evidence first| Signal | Llama 3.1 Nemotron Nano VL 8B v1 | ShieldGemma 9B |
|---|---|---|
| Decision fit | Vision | Classification |
| Context window | 4K | 8K |
| Cheapest output | - | - |
| Provider routes | 1 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 3.1 Nemotron Nano VL 8B v1 uniquely exposes Vision and Multimodal in local model data.
- Local decision data tags Llama 3.1 Nemotron Nano VL 8B v1 for Vision.
- 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 prices on this page.
Llama 3.1 Nemotron Nano VL 8B v1
Unavailable
No complete token price in local provider data
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
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
- Check replacement coverage for Vision and Multimodal before moving production traffic.
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
- Llama 3.1 Nemotron Nano VL 8B v1 adds Vision and Multimodal in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-03-01 | 2024-07-01 |
| Context window | 4K | 8K |
| Parameters | 8B | 9B |
| Architecture | decoder only | decoder only |
| License | 1 | 1 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Llama 3.1 Nemotron Nano VL 8B v1 | ShieldGemma 9B |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers |
Pricing not yet sourced for either model.
Capabilities
| Capability | Llama 3.1 Nemotron Nano VL 8B v1 | ShieldGemma 9B |
|---|---|---|
| Vision | Yes | No |
| Multimodal | Yes | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | No |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: Llama 3.1 Nemotron Nano VL 8B v1 and multimodal input: Llama 3.1 Nemotron Nano VL 8B v1. 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 3.1 Nemotron Nano VL 8B v1 has no token price sourced yet 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 3.1 Nemotron Nano VL 8B v1 when vision-heavy evaluation 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.
FAQ
Which has a larger context window, Llama 3.1 Nemotron Nano VL 8B v1 or ShieldGemma 9B?
ShieldGemma 9B supports 8K tokens, while Llama 3.1 Nemotron Nano VL 8B 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 3.1 Nemotron Nano VL 8B v1 or ShieldGemma 9B open source?
Llama 3.1 Nemotron Nano VL 8B v1 is listed under 1. 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 vision, Llama 3.1 Nemotron Nano VL 8B v1 or ShieldGemma 9B?
Llama 3.1 Nemotron Nano VL 8B v1 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.
Which is better for multimodal input, Llama 3.1 Nemotron Nano VL 8B v1 or ShieldGemma 9B?
Llama 3.1 Nemotron Nano VL 8B v1 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.
Where can I run Llama 3.1 Nemotron Nano VL 8B v1 and ShieldGemma 9B?
Llama 3.1 Nemotron Nano VL 8B v1 is available on NVIDIA NIM. ShieldGemma 9B is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.1 Nemotron Nano VL 8B v1 over ShieldGemma 9B?
Llama 3.1 Nemotron Nano VL 8B v1 is safer overall; choose ShieldGemma 9B when long-context analysis matters. If your workload also depends on vision-heavy evaluation, start with Llama 3.1 Nemotron Nano VL 8B v1; if it depends on long-context analysis, run the same evaluation with ShieldGemma 9B.
Continue comparing
Last reviewed: 2026-05-01. Data sourced from public model cards and provider documentation.