LLM Reference

Gemma 4 12B vs Nemotron 3 Nano Omni

Gemma 4 12B (2026) and Nemotron 3 Nano Omni (2026) are frontier reasoning models from Google DeepMind and NVIDIA AI. Gemma 4 12B ships a 256k-token context window, while Nemotron 3 Nano Omni ships a 262k-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.

Gemma 4 12B is safer overall; choose Nemotron 3 Nano Omni when long-context analysis matters.

Decision scorecard

Local evidence first
SignalGemma 4 12BNemotron 3 Nano Omni
Best forreasoning-heavy apps, multimodal apps, and tool-calling agentsmultimodal apps
Decision fitRAG, Agents, and Long contextLong context, Vision, and Classification
Context window256k262k
Cheapest output--
Provider routes2 tracked1 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose Gemma 4 12B when...
  • Gemma 4 12B has broader tracked provider coverage for fallback and procurement flexibility.
  • Gemma 4 12B uniquely exposes Vision, Reasoning, and Function calling in local model data.
  • Local decision data tags Gemma 4 12B for RAG, Agents, and Long context.
Choose Nemotron 3 Nano Omni when...
  • Nemotron 3 Nano Omni has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • Local decision data tags Nemotron 3 Nano Omni for Long context, Vision, and Classification.

Monthly cost at traffic

Estimate token spend from the cheapest tracked input and output route or tier on this page.

Gemma 4 12B

Unavailable

No complete token price in local provider data

Nemotron 3 Nano Omni

Unavailable

No complete token price in local provider data

Cost delta unavailable until both models have sourced input and output token prices.

Switch friction

Gemma 4 12B -> Nemotron 3 Nano Omni
  • No overlapping tracked provider route is sourced for Gemma 4 12B and Nemotron 3 Nano Omni; plan for SDK, billing, or endpoint changes.
  • Check replacement coverage for Vision, Reasoning, and Function calling before moving production traffic.
Nemotron 3 Nano Omni -> Gemma 4 12B
  • No overlapping tracked provider route is sourced for Nemotron 3 Nano Omni and Gemma 4 12B; plan for SDK, billing, or endpoint changes.
  • Gemma 4 12B adds Vision, Reasoning, and Function calling in local capability data.

Specs

Specification
Released2026-06-032026-04-28
Context window256k262k
Parameters11.9B30B
Architectureencoder free unified multimodalHybrid Mamba-Transformer MoE
LicenseApache 2.0NVIDIA Open Model
Knowledge cutoff2025-01-

Pricing and availability

Pricing attributeGemma 4 12BNemotron 3 Nano Omni
Input price--
Output price--
Providers

Pricing not yet sourced for either model.

Capabilities

CapabilityGemma 4 12BNemotron 3 Nano Omni
VisionYesNo
MultimodalYesYes
ReasoningYesNo
Function callingYesNo
Tool useYesNo
Structured outputsNoNo
Code executionNoNo
IDE integrationNoNo
Computer useNoNo
Parallel agentsNoNo

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on vision: Gemma 4 12B, reasoning mode: Gemma 4 12B, function calling: Gemma 4 12B, and tool use: Gemma 4 12B. Both models share 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: Gemma 4 12B has no token price sourced yet and Nemotron 3 Nano Omni has no token price sourced yet. Provider availability is 2 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.

Choose Gemma 4 12B when reasoning depth and broader provider choice are central to the workload. Choose Nemotron 3 Nano Omni 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, Gemma 4 12B or Nemotron 3 Nano Omni?

Nemotron 3 Nano Omni supports 262k tokens, while Gemma 4 12B supports 256k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.

Is Gemma 4 12B or Nemotron 3 Nano Omni open source?

Gemma 4 12B is listed under Apache 2.0. Nemotron 3 Nano Omni is listed under NVIDIA Open Model. 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, Gemma 4 12B or Nemotron 3 Nano Omni?

Gemma 4 12B 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, Gemma 4 12B or Nemotron 3 Nano Omni?

Both Gemma 4 12B and Nemotron 3 Nano Omni 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 reasoning mode, Gemma 4 12B or Nemotron 3 Nano Omni?

Gemma 4 12B has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Where can I run Gemma 4 12B and Nemotron 3 Nano Omni?

Gemma 4 12B is available on Hugging Face Inference Endpoints and Kaggle Models. Nemotron 3 Nano Omni is available on OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

Continue comparing

Last reviewed: 2026-06-03. Data sourced from public model cards and provider documentation.