LLM Reference

Gemma 3 12B vs Nemotron-Nano-9B-v2

Gemma 3 12B (2026) and Nemotron-Nano-9B-v2 (2025) are compact production models from Google DeepMind and NVIDIA AI. Gemma 3 12B ships a 33k-token context window, while Nemotron-Nano-9B-v2 ships a not-yet-sourced context window. On pricing, both list $0.04/1M input tokens on the cheapest tracked route. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.

Gemma 3 12B is safer overall; choose Nemotron-Nano-9B-v2 when provider fit matters.

Decision scorecard

Local evidence first
SignalGemma 3 12BNemotron-Nano-9B-v2
Best forprovider-routed productionprovider-routed production
Decision fitClassification and JSON / Tool useClassification and JSON / Tool use
Context window33k
Cheapest output$0.13/1M tokens$0.16/1M tokens
Provider routes5 tracked3 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose Gemma 3 12B when...
  • Gemma 3 12B has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • Gemma 3 12B has the lower cheapest tracked output price at $0.13/1M tokens.
  • Gemma 3 12B has broader tracked provider coverage for fallback and procurement flexibility.
  • Local decision data tags Gemma 3 12B for Classification and JSON / Tool use.
Choose Nemotron-Nano-9B-v2 when...
  • Local decision data tags Nemotron-Nano-9B-v2 for Classification and JSON / Tool use.

Monthly cost at traffic

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

Lower estimate Gemma 3 12B

Gemma 3 12B

$64.50

Cheapest tracked route/tier: OpenRouter

Nemotron-Nano-9B-v2

$72.00

Cheapest tracked route/tier: OpenRouter

Estimated monthly gap: $7.50. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.

Switch friction

Gemma 3 12B -> Nemotron-Nano-9B-v2
  • Provider overlap exists on OpenRouter; start route-level A/B tests there.
  • Nemotron-Nano-9B-v2 is $0.03/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
Nemotron-Nano-9B-v2 -> Gemma 3 12B
  • Provider overlap exists on OpenRouter; start route-level A/B tests there.
  • Gemma 3 12B is $0.03/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.

Specs

Specification
Released2026-01-012025-08-18
Context window33k
Parameters12B9B
Architecturedecoder onlydecoder only
LicenseGemmaLlama 3 Community
OpennessOpen weightsOpen weights
Commercial useCommercial use with conditionsCommercial use with conditions
Knowledge cutoff2024-082025-03

Pricing and availability

Pricing attributeGemma 3 12BNemotron-Nano-9B-v2
Input price$0.04/1M tokens$0.04/1M tokens
Output price$0.13/1M tokens$0.16/1M tokens
Providers

Capabilities

CapabilityGemma 3 12BNemotron-Nano-9B-v2
VisionNoNo
MultimodalNoNo
ReasoningNoNo
Function callingNoNo
Tool useNoNo
Structured outputsYesYes
Code executionNoNo
IDE integrationNoNo
Computer useNoNo
Parallel agentsNoNo

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint is close: both models cover structured outputs. 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.

For cost, Gemma 3 12B lists $0.04/1M input and $0.13/1M output tokens on the cheapest tracked provider, while Nemotron-Nano-9B-v2 lists $0.04/1M input and $0.16/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Gemma 3 12B lower by about $0.01 per million blended tokens. Availability is 5 providers versus 3, so concentration risk also matters.

Choose Gemma 3 12B when provider fit and broader provider choice are central to the workload. Choose Nemotron-Nano-9B-v2 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 is cheaper, Gemma 3 12B or Nemotron-Nano-9B-v2?

Gemma 3 12B is cheaper on tracked token pricing. Gemma 3 12B costs $0.04/1M input and $0.13/1M output tokens. Nemotron-Nano-9B-v2 costs $0.04/1M input and $0.16/1M output tokens. Provider discounts or batch pricing can still change the final bill.

Is Gemma 3 12B or Nemotron-Nano-9B-v2 open source?

Gemma 3 12B is listed under Gemma. Nemotron-Nano-9B-v2 is listed under Llama 3 Community. 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 structured outputs, Gemma 3 12B or Nemotron-Nano-9B-v2?

Both Gemma 3 12B and Nemotron-Nano-9B-v2 expose structured outputs. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.

Where can I run Gemma 3 12B and Nemotron-Nano-9B-v2?

Gemma 3 12B is available on Cloudflare Workers AI, AWS Bedrock, OpenRouter, GCP Vertex AI, and Novita AI. Nemotron-Nano-9B-v2 is available on NVIDIA NIM, OpenRouter, and Vercel AI Gateway. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

When should I pick Gemma 3 12B over Nemotron-Nano-9B-v2?

Gemma 3 12B is safer overall; choose Nemotron-Nano-9B-v2 when provider fit matters. If your workload also depends on provider fit, start with Gemma 3 12B; if it depends on provider fit, run the same evaluation with Nemotron-Nano-9B-v2.

Continue comparing

Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.