Nemotron-Nano-12B-v2-VL vs NV-EmbedCode 7B v1
Nemotron-Nano-12B-v2-VL (2025) and NV-EmbedCode 7B v1 (2025) are compact production models from NVIDIA AI. Nemotron-Nano-12B-v2-VL ships a not-yet-sourced context window, while NV-EmbedCode 7B v1 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. The goal is to make the tradeoff clear before deeper testing.
Nemotron-Nano-12B-v2-VL is safer overall; choose NV-EmbedCode 7B v1 when provider fit matters.
Decision scorecard
Local evidence first| Signal | Nemotron-Nano-12B-v2-VL | NV-EmbedCode 7B v1 |
|---|---|---|
| Decision fit | Vision and JSON / Tool use | General |
| Context window | — | 4K |
| Cheapest output | $0.6/1M tokens | - |
| Provider routes | 2 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Nemotron-Nano-12B-v2-VL has broader tracked provider coverage for fallback and procurement flexibility.
- Nemotron-Nano-12B-v2-VL uniquely exposes Vision, Multimodal, and Structured outputs in local model data.
- Local decision data tags Nemotron-Nano-12B-v2-VL for Vision and JSON / Tool use.
- NV-EmbedCode 7B 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 prices on this page.
Nemotron-Nano-12B-v2-VL
$310
Cheapest tracked route: OpenRouter
NV-EmbedCode 7B 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.
- Check replacement coverage for Vision, Multimodal, and Structured outputs before moving production traffic.
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
- Nemotron-Nano-12B-v2-VL adds Vision, Multimodal, and Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-10-28 | 2025-06-01 |
| Context window | — | 4K |
| Parameters | 12B | 7B |
| Architecture | decoder only | encoder |
| License | Unknown | 1 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Nemotron-Nano-12B-v2-VL | NV-EmbedCode 7B v1 |
|---|---|---|
| Input price | $0.2/1M tokens | - |
| Output price | $0.6/1M tokens | - |
| Providers |
Capabilities
| Capability | Nemotron-Nano-12B-v2-VL | NV-EmbedCode 7B v1 |
|---|---|---|
| Vision | Yes | No |
| Multimodal | Yes | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | No |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: Nemotron-Nano-12B-v2-VL, multimodal input: Nemotron-Nano-12B-v2-VL, and structured outputs: Nemotron-Nano-12B-v2-VL. 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: Nemotron-Nano-12B-v2-VL has $0.2/1M input tokens and NV-EmbedCode 7B v1 has no token price sourced yet. Provider availability is 2 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Nemotron-Nano-12B-v2-VL when vision-heavy evaluation and broader provider choice are central to the workload. Choose NV-EmbedCode 7B 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 Nemotron-Nano-12B-v2-VL or NV-EmbedCode 7B v1 open source?
Nemotron-Nano-12B-v2-VL is listed under Unknown. NV-EmbedCode 7B 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.
Which is better for vision, Nemotron-Nano-12B-v2-VL or NV-EmbedCode 7B v1?
Nemotron-Nano-12B-v2-VL has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, Nemotron-Nano-12B-v2-VL or NV-EmbedCode 7B v1?
Nemotron-Nano-12B-v2-VL has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for structured outputs, Nemotron-Nano-12B-v2-VL or NV-EmbedCode 7B v1?
Nemotron-Nano-12B-v2-VL 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 Nemotron-Nano-12B-v2-VL and NV-EmbedCode 7B v1?
Nemotron-Nano-12B-v2-VL is available on NVIDIA NIM and OpenRouter. NV-EmbedCode 7B v1 is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
When should I pick Nemotron-Nano-12B-v2-VL over NV-EmbedCode 7B v1?
Nemotron-Nano-12B-v2-VL is safer overall; choose NV-EmbedCode 7B v1 when provider fit matters. If your workload also depends on vision-heavy evaluation, start with Nemotron-Nano-12B-v2-VL; if it depends on provider fit, run the same evaluation with NV-EmbedCode 7B v1.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.