LLM Reference

Llama 3.1 Nemotron Nano VL 8B v1 vs ShieldGemma 2

Llama 3.1 Nemotron Nano VL 8B v1 (2025) and ShieldGemma 2 (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 2 ships a not-yet-sourced context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.

Llama 3.1 Nemotron Nano VL 8B v1 is safer overall; choose ShieldGemma 2 when vision-heavy evaluation matters.

Decision scorecard

Local evidence first
SignalLlama 3.1 Nemotron Nano VL 8B v1ShieldGemma 2
Best formultimodal appsmultimodal apps and tool-calling agents
Decision fitVisionAgents, Vision, and Classification
Context window4k
Cheapest output--
Provider routes1 tracked1 tracked
Shared benchmarks0 shared0 shared

Decision tradeoffs

Choose Llama 3.1 Nemotron Nano VL 8B v1 when...
  • Llama 3.1 Nemotron Nano VL 8B v1 has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • Local decision data tags Llama 3.1 Nemotron Nano VL 8B v1 for Vision.
Choose ShieldGemma 2 when...
  • ShieldGemma 2 uniquely exposes Function calling, Tool use, and Structured outputs 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 route or tier on this page.

Llama 3.1 Nemotron Nano VL 8B 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

Llama 3.1 Nemotron Nano VL 8B v1 -> ShieldGemma 2
  • No overlapping tracked provider route is sourced for Llama 3.1 Nemotron Nano VL 8B v1 and ShieldGemma 2; plan for SDK, billing, or endpoint changes.
  • ShieldGemma 2 adds Function calling, Tool use, and Structured outputs in local capability data.
ShieldGemma 2 -> Llama 3.1 Nemotron Nano VL 8B v1
  • No overlapping tracked provider route is sourced for ShieldGemma 2 and Llama 3.1 Nemotron Nano VL 8B v1; plan for SDK, billing, or endpoint changes.
  • Check replacement coverage for Function calling, Tool use, and Structured outputs before moving production traffic.

Specs

Specification
Released2025-03-012024-09-01
Context window4k
Parameters8B4B
ArchitectureDecoder OnlyDecoder Only
LicenseLlama 3 CommunityProprietary
OpennessOpen weightsProprietary
Commercial useCommercial use: conditionalCommercial use: conditional
Knowledge cutoff--

Pricing and availability

Pricing attributeLlama 3.1 Nemotron Nano VL 8B v1ShieldGemma 2
Input price--
Output price--
Providers

Pricing not yet sourced for either model.

Capabilities

CapabilityLlama 3.1 Nemotron Nano VL 8B v1ShieldGemma 2
VisionYesYes
MultimodalYesYes
ReasoningNoNo
Function callingNoYes
Tool useNoYes
Structured outputsNoYes
Code executionNoNo
IDE integrationNoNo
Computer useNoNo
Parallel agentsNoNo

Benchmarks

No shared benchmark scores are currently available for this pair.

Deep dive

The capability footprint differs most on function calling: ShieldGemma 2, tool use: ShieldGemma 2, and structured outputs: ShieldGemma 2. Both models share vision and multimodal input, 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 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 3.1 Nemotron Nano VL 8B v1 when vision-heavy evaluation 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 3.1 Nemotron Nano VL 8B v1 or ShieldGemma 2 open source?

Llama 3.1 Nemotron Nano VL 8B v1 is listed under Llama 3 Community. 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 3.1 Nemotron Nano VL 8B v1 or ShieldGemma 2?

Both Llama 3.1 Nemotron Nano VL 8B v1 and ShieldGemma 2 expose vision. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.

Which is better for multimodal input, Llama 3.1 Nemotron Nano VL 8B v1 or ShieldGemma 2?

Both Llama 3.1 Nemotron Nano VL 8B v1 and ShieldGemma 2 expose multimodal input. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.

Which is better for function calling, Llama 3.1 Nemotron Nano VL 8B 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 3.1 Nemotron Nano VL 8B 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 3.1 Nemotron Nano VL 8B v1 and ShieldGemma 2?

Llama 3.1 Nemotron Nano VL 8B 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: 2026-06-15. Data sourced from public model cards and provider documentation.