Llama 3.2 NV EmbedQA 1B v2 vs Nemotron 3 Ultra
Llama 3.2 NV EmbedQA 1B v2 (2025) and Nemotron 3 Ultra (2024) are compact production models from NVIDIA AI. Llama 3.2 NV EmbedQA 1B v2 ships a 4K-token context window, while Nemotron 3 Ultra ships a 128K-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.
Nemotron 3 Ultra fits 32x more tokens; pick it for long-context work and Llama 3.2 NV EmbedQA 1B v2 for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama 3.2 NV EmbedQA 1B v2 | Nemotron 3 Ultra |
|---|---|---|
| Decision fit | General | Long context |
| Context window | 4K | 128K |
| Cheapest output | - | - |
| Provider routes | 1 tracked | 0 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 3.2 NV EmbedQA 1B v2 has broader tracked provider coverage for fallback and procurement flexibility.
- Nemotron 3 Ultra has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Nemotron 3 Ultra for Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Llama 3.2 NV EmbedQA 1B v2
Unavailable
No complete token price in local provider data
Nemotron 3 Ultra
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 Llama 3.2 NV EmbedQA 1B v2 and Nemotron 3 Ultra; plan for SDK, billing, or endpoint changes.
- No overlapping tracked provider route is sourced for Nemotron 3 Ultra and Llama 3.2 NV EmbedQA 1B v2; plan for SDK, billing, or endpoint changes.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-03-01 | 2024-09-10 |
| Context window | 4K | 128K |
| Parameters | 1B | — |
| Architecture | encoder | decoder only |
| License | 1 | Unknown |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Llama 3.2 NV EmbedQA 1B v2 | Nemotron 3 Ultra |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers | - |
Pricing not yet sourced for either model.
Capabilities
| Capability | Llama 3.2 NV EmbedQA 1B v2 | Nemotron 3 Ultra |
|---|---|---|
| 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: Llama 3.2 NV EmbedQA 1B v2 has no token price sourced yet and Nemotron 3 Ultra has no token price sourced yet. Provider availability is 1 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Llama 3.2 NV EmbedQA 1B v2 when provider fit and broader provider choice are central to the workload. Choose Nemotron 3 Ultra when long-context analysis and larger context windows 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, Llama 3.2 NV EmbedQA 1B v2 or Nemotron 3 Ultra?
Nemotron 3 Ultra supports 128K tokens, while Llama 3.2 NV EmbedQA 1B v2 supports 4K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Llama 3.2 NV EmbedQA 1B v2 or Nemotron 3 Ultra open source?
Llama 3.2 NV EmbedQA 1B v2 is listed under 1. Nemotron 3 Ultra is listed under Unknown. 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 Llama 3.2 NV EmbedQA 1B v2 and Nemotron 3 Ultra?
Llama 3.2 NV EmbedQA 1B v2 is available on NVIDIA NIM. Nemotron 3 Ultra is available on the tracked providers still being sourced. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.2 NV EmbedQA 1B v2 over Nemotron 3 Ultra?
Nemotron 3 Ultra fits 32x more tokens; pick it for long-context work and Llama 3.2 NV EmbedQA 1B v2 for tighter calls. If your workload also depends on provider fit, start with Llama 3.2 NV EmbedQA 1B v2; if it depends on long-context analysis, run the same evaluation with Nemotron 3 Ultra.
Continue comparing
Last reviewed: 2026-05-14. Data sourced from public model cards and provider documentation.