Gemma 3 vs Nemotron-Nano-9B-v2
Gemma 3 (2025) and Nemotron-Nano-9B-v2 (2025) are general-purpose language models from Google DeepMind and NVIDIA AI. Gemma 3 ships a not-yet-sourced 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.
Nemotron-Nano-9B-v2 is safer overall; choose Gemma 3 when provider fit matters.
Decision scorecard
Local evidence first| Signal | Gemma 3 | Nemotron-Nano-9B-v2 |
|---|---|---|
| Best for | provider-routed production | provider-routed production |
| Decision fit | Classification and JSON / Tool use | Classification and JSON / Tool use |
| Context window | — | — |
| Cheapest output | $0.08/1M tokens | $0.16/1M tokens |
| Provider routes | 3 tracked | 3 tracked |
| Shared benchmarks | 0 shared | 0 shared |
Decision tradeoffs
- Gemma 3 has the lower cheapest tracked output price at $0.08/1M tokens.
- Local decision data tags Gemma 3 for Classification and JSON / Tool use.
- 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.
Gemma 3
$52.00
Cheapest tracked route/tier: OpenRouter
Nemotron-Nano-9B-v2
$72.00
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $20.00. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- Nemotron-Nano-9B-v2 is $0.08/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- Gemma 3 is $0.08/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-03-12 | 2025-08-18 |
| Context window | — | — |
| Parameters | — | 9B |
| Architecture | Decoder Only | Decoder Only |
| License | Gemma | Llama 3 Community |
| Openness | Open weights | Open weights |
| Commercial use | Commercial use: conditional | Commercial use: conditional |
| Knowledge cutoff | 2025-01 | 2025-03 |
Pricing and availability
| Pricing attribute | Gemma 3 | Nemotron-Nano-9B-v2 |
|---|---|---|
| Input price | $0.04/1M tokens | $0.04/1M tokens |
| Output price | $0.08/1M tokens | $0.16/1M tokens |
| Providers |
Capabilities
| Capability | Gemma 3 | Nemotron-Nano-9B-v2 |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark scores are currently available 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 lists $0.04/1M input and $0.08/1M 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 lower by about $0.02 per million blended tokens. Availability is 3 providers versus 3, so concentration risk also matters.
Choose Gemma 3 when provider fit 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 or Nemotron-Nano-9B-v2?
Gemma 3 is cheaper on tracked token pricing. Gemma 3 costs $0.04/1M input and $0.08/1M 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 or Nemotron-Nano-9B-v2 open source?
Gemma 3 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 or Nemotron-Nano-9B-v2?
Both Gemma 3 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 and Nemotron-Nano-9B-v2?
Gemma 3 is available on OpenRouter, Google AI Studio, and GCP Vertex 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 over Nemotron-Nano-9B-v2?
Nemotron-Nano-9B-v2 is safer overall; choose Gemma 3 when provider fit matters. If your workload also depends on provider fit, start with Gemma 3; if it depends on provider fit, run the same evaluation with Nemotron-Nano-9B-v2.
Continue comparing
Last reviewed: 2026-06-15. Data sourced from public model cards and provider documentation.