Gemma 3n 2B (free) vs Llama 3.2 NV EmbedQA 1B v1
Gemma 3n 2B (free) (2025) and Llama 3.2 NV EmbedQA 1B v1 (2024) are compact production models from Google DeepMind and NVIDIA AI. Gemma 3n 2B (free) ships a 8K-token context window, while Llama 3.2 NV EmbedQA 1B v1 ships a 512-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Gemma 3n 2B (free) fits 16x more tokens; pick it for long-context work and Llama 3.2 NV EmbedQA 1B v1 for tighter calls.
Decision scorecard
Local evidence first| Signal | Gemma 3n 2B (free) | Llama 3.2 NV EmbedQA 1B v1 |
|---|---|---|
| Decision fit | General | General |
| Context window | 8K | 512 |
| Cheapest output | - | - |
| Provider routes | 1 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Gemma 3n 2B (free) has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Use Llama 3.2 NV EmbedQA 1B v1 when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Gemma 3n 2B (free)
Unavailable
No complete token price in local provider data
Llama 3.2 NV EmbedQA 1B 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
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-04-03 | 2024-10-08 |
| Context window | 8K | 512 |
| Parameters | — | 1B |
| Architecture | decoder only | encoder |
| License | Open Source | 1 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Gemma 3n 2B (free) | Llama 3.2 NV EmbedQA 1B v1 |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers |
Pricing not yet sourced for either model.
Capabilities
| Capability | Gemma 3n 2B (free) | Llama 3.2 NV EmbedQA 1B 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 |
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 3n 2B (free) has no token price sourced yet and Llama 3.2 NV EmbedQA 1B v1 has no token price sourced yet. Provider availability is 1 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 3n 2B (free) when long-context analysis and larger context windows are central to the workload. Choose Llama 3.2 NV EmbedQA 1B 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.
FAQ
Which has a larger context window, Gemma 3n 2B (free) or Llama 3.2 NV EmbedQA 1B v1?
Gemma 3n 2B (free) supports 8K tokens, while Llama 3.2 NV EmbedQA 1B v1 supports 512 tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Gemma 3n 2B (free) or Llama 3.2 NV EmbedQA 1B v1 open source?
Gemma 3n 2B (free) is listed under Open Source. Llama 3.2 NV EmbedQA 1B v1 is listed under 1. 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 3n 2B (free) and Llama 3.2 NV EmbedQA 1B v1?
Gemma 3n 2B (free) is available on NVIDIA NIM. Llama 3.2 NV EmbedQA 1B v1 is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Gemma 3n 2B (free) over Llama 3.2 NV EmbedQA 1B v1?
Gemma 3n 2B (free) fits 16x more tokens; pick it for long-context work and Llama 3.2 NV EmbedQA 1B v1 for tighter calls. If your workload also depends on long-context analysis, start with Gemma 3n 2B (free); if it depends on provider fit, run the same evaluation with Llama 3.2 NV EmbedQA 1B v1.
Continue comparing
Last reviewed: 2026-05-06. Data sourced from public model cards and provider documentation.