Gemma 2 2B vs Llama 3.1 Nemotron Nano 8B v1
Gemma 2 2B (2024) and Llama 3.1 Nemotron Nano 8B v1 (2025) are compact production models from Google DeepMind and NVIDIA AI. Gemma 2 2B ships a 8k-token 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.
Llama 3.1 Nemotron Nano 8B v1 is safer overall; choose Gemma 2 2B when long-context analysis matters.
Decision scorecard
Local evidence first| Signal | Gemma 2 2B | Llama 3.1 Nemotron Nano 8B v1 |
|---|---|---|
| Best for | general production evaluation | general production evaluation |
| Decision fit | General | General |
| Context window | 8k | 4k |
| Cheapest output | - | - |
| Provider routes | 0 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Gemma 2 2B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 3.1 Nemotron Nano 8B v1 has broader tracked provider coverage for fallback and procurement flexibility.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Gemma 2 2B
Unavailable
No complete token price in local provider data
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 2 2B and Llama 3.1 Nemotron Nano 8B v1; plan for SDK, billing, or endpoint changes.
- No overlapping tracked provider route is sourced for Llama 3.1 Nemotron Nano 8B v1 and Gemma 2 2B; plan for SDK, billing, or endpoint changes.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-07-31 | 2025-03-01 |
| Context window | 8k | 4k |
| Parameters | 2B | 8B |
| Architecture | decoder only | decoder only |
| License | Gemma | Llama 3 Community |
| Openness | Open weights | Open weights |
| Commercial use | Commercial use with conditions | Commercial use with conditions |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Gemma 2 2B | Llama 3.1 Nemotron Nano 8B v1 |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers | - |
Pricing not yet sourced for either model.
Capabilities
| Capability | Gemma 2 2B | 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 | No | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint is close: both models cover the core production surface. 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.
Pricing coverage is uneven: Gemma 2 2B has no token price sourced yet and Llama 3.1 Nemotron Nano 8B v1 has no token price sourced yet. Provider availability is 0 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 2 2B when long-context analysis and larger context windows are central to the workload. Choose Llama 3.1 Nemotron Nano 8B v1 when provider fit and broader provider choice 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 2 2B or Llama 3.1 Nemotron Nano 8B v1?
Gemma 2 2B supports 8k tokens, while Llama 3.1 Nemotron Nano 8B v1 supports 4k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Gemma 2 2B or Llama 3.1 Nemotron Nano 8B v1 open source?
Gemma 2 2B 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.
Where can I run Gemma 2 2B and Llama 3.1 Nemotron Nano 8B v1?
Gemma 2 2B is available on the tracked providers still being sourced. 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 2 2B over Llama 3.1 Nemotron Nano 8B v1?
Llama 3.1 Nemotron Nano 8B v1 is safer overall; choose Gemma 2 2B when long-context analysis matters. If your workload also depends on long-context analysis, start with Gemma 2 2B; if it depends on provider fit, run the same evaluation with Llama 3.1 Nemotron Nano 8B v1.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.