Llama 3.2 NV EmbedQA 1B v1 vs Nemotron Mini 4B Instruct
Llama 3.2 NV EmbedQA 1B v1 (2024) and Nemotron Mini 4B Instruct (2024) are compact production models from NVIDIA AI. Llama 3.2 NV EmbedQA 1B v1 ships a 512-token context window, while Nemotron Mini 4B Instruct ships a 4k-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 Mini 4B Instruct fits 8x 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 | Llama 3.2 NV EmbedQA 1B v1 | Nemotron Mini 4B Instruct |
|---|---|---|
| Best for | general production evaluation | general production evaluation |
| Decision fit | General | General |
| Context window | 512 | 4k |
| Cheapest output | - | - |
| Provider routes | 1 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- 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.
- Nemotron Mini 4B Instruct 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.
Llama 3.2 NV EmbedQA 1B v1
Unavailable
No complete token price in local provider data
Nemotron Mini 4B Instruct
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 | 2024-10-08 | 2024-08-01 |
| Context window | 512 | 4k |
| Parameters | 1B | 4B |
| Architecture | encoder | decoder only |
| License | 1 | 1 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Llama 3.2 NV EmbedQA 1B v1 | Nemotron Mini 4B Instruct |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers |
Pricing not yet sourced for either model.
Capabilities
| Capability | Llama 3.2 NV EmbedQA 1B v1 | Nemotron Mini 4B Instruct |
|---|---|---|
| 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: Llama 3.2 NV EmbedQA 1B v1 has no token price sourced yet and Nemotron Mini 4B Instruct 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 Llama 3.2 NV EmbedQA 1B v1 when provider fit are central to the workload. Choose Nemotron Mini 4B Instruct 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 v1 or Nemotron Mini 4B Instruct?
Nemotron Mini 4B Instruct supports 4k 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 Llama 3.2 NV EmbedQA 1B v1 or Nemotron Mini 4B Instruct open source?
Llama 3.2 NV EmbedQA 1B v1 is listed under 1. Nemotron Mini 4B Instruct 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 Llama 3.2 NV EmbedQA 1B v1 and Nemotron Mini 4B Instruct?
Llama 3.2 NV EmbedQA 1B v1 is available on NVIDIA NIM. Nemotron Mini 4B Instruct is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.2 NV EmbedQA 1B v1 over Nemotron Mini 4B Instruct?
Nemotron Mini 4B Instruct fits 8x 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 provider fit, start with Llama 3.2 NV EmbedQA 1B v1; if it depends on long-context analysis, run the same evaluation with Nemotron Mini 4B Instruct.
Continue comparing
Last reviewed: 2026-05-06. Data sourced from public model cards and provider documentation.