Gemma 3 vs Llama 3.1 Nemotron Nano 8B v1
Gemma 3 (2025) and Llama 3.1 Nemotron Nano 8B v1 (2025) are compact production models from Google DeepMind and NVIDIA AI. Gemma 3 ships a not-yet-sourced context window, while Llama 3.1 Nemotron Nano 8B v1 ships a 4k-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 3 is safer overall; choose Llama 3.1 Nemotron Nano 8B v1 when provider fit matters.
Decision scorecard
Local evidence first| Signal | Gemma 3 | Llama 3.1 Nemotron Nano 8B v1 |
|---|---|---|
| Best for | provider-routed production | general production evaluation |
| Decision fit | Classification and JSON / Tool use | General |
| Context window | — | 4k |
| Cheapest output | $0.08/1M tokens | - |
| Provider routes | 3 tracked | 1 tracked |
| Shared benchmarks | 0 shared | 0 shared |
Decision tradeoffs
- Gemma 3 has broader tracked provider coverage for fallback and procurement flexibility.
- Gemma 3 uniquely exposes Structured outputs in local model data.
- Local decision data tags Gemma 3 for Classification and JSON / Tool use.
- Llama 3.1 Nemotron Nano 8B v1 has the larger context window for long prompts, retrieval packs, or transcript analysis.
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
Llama 3.1 Nemotron Nano 8B v1
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Gemma 3 and Llama 3.1 Nemotron Nano 8B v1; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Structured outputs before moving production traffic.
- No overlapping tracked provider route is sourced for Llama 3.1 Nemotron Nano 8B v1 and Gemma 3; plan for SDK, billing, or endpoint changes.
- Gemma 3 adds Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-03-12 | 2025-03-01 |
| Context window | — | 4k |
| Parameters | — | 8B |
| 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 | - |
Pricing and availability
| Pricing attribute | Gemma 3 | Llama 3.1 Nemotron Nano 8B v1 |
|---|---|---|
| Input price | $0.04/1M tokens | - |
| Output price | $0.08/1M tokens | - |
| Providers |
Capabilities
| Capability | Gemma 3 | Llama 3.1 Nemotron Nano 8B v1 |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | No |
| 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 differs most on structured outputs: Gemma 3. 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: Gemma 3 has $0.04/1M input tokens and Llama 3.1 Nemotron Nano 8B v1 has no token price sourced yet. Provider availability is 3 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 3 when provider fit and broader provider choice are central to the workload. Choose Llama 3.1 Nemotron Nano 8B v1 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
Is Gemma 3 or Llama 3.1 Nemotron Nano 8B v1 open source?
Gemma 3 is listed under Gemma. Llama 3.1 Nemotron Nano 8B v1 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 Llama 3.1 Nemotron Nano 8B v1?
Gemma 3 has the clearer documented structured outputs signal in this comparison. If structured outputs is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Gemma 3 and Llama 3.1 Nemotron Nano 8B v1?
Gemma 3 is available on OpenRouter, Google AI Studio, and GCP Vertex AI. Llama 3.1 Nemotron Nano 8B v1 is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Gemma 3 over Llama 3.1 Nemotron Nano 8B v1?
Gemma 3 is safer overall; choose Llama 3.1 Nemotron Nano 8B v1 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 Llama 3.1 Nemotron Nano 8B v1.
Continue comparing
Last reviewed: 2026-06-15. Data sourced from public model cards and provider documentation.