Llama 3.1 405B Instruct vs Nemotron-Nano-12B-v2-VL
Llama 3.1 405B Instruct (2024) and Nemotron-Nano-12B-v2-VL (2025) are compact production models from AI at Meta and NVIDIA AI. Llama 3.1 405B Instruct ships a 128K-token context window, while Nemotron-Nano-12B-v2-VL ships a not-yet-sourced context window. On pricing, Nemotron-Nano-12B-v2-VL costs $0.2/1M input tokens versus $2.4/1M for the alternative. 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-Nano-12B-v2-VL is ~1100% cheaper at $0.2/1M; pay for Llama 3.1 405B Instruct only for provider fit.
Decision scorecard
Local evidence first| Signal | Llama 3.1 405B Instruct | Nemotron-Nano-12B-v2-VL |
|---|---|---|
| Decision fit | RAG, Long context, and Classification | Vision and JSON / Tool use |
| Context window | 128K | — |
| Cheapest output | $2.4/1M tokens | $0.6/1M tokens |
| Provider routes | 11 tracked | 2 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 3.1 405B Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 3.1 405B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Llama 3.1 405B Instruct for RAG, Long context, and Classification.
- Nemotron-Nano-12B-v2-VL has the lower cheapest tracked output price at $0.6/1M tokens.
- Nemotron-Nano-12B-v2-VL uniquely exposes Vision and Multimodal in local model data.
- Local decision data tags Nemotron-Nano-12B-v2-VL for Vision and JSON / Tool use.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Llama 3.1 405B Instruct
$2,520
Cheapest tracked route: AWS Bedrock
Nemotron-Nano-12B-v2-VL
$310
Cheapest tracked route: OpenRouter
Estimated monthly gap: $2,210. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
- Nemotron-Nano-12B-v2-VL is $1.8/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Nemotron-Nano-12B-v2-VL adds Vision and Multimodal in local capability data.
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
- Llama 3.1 405B Instruct is $1.8/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Vision and Multimodal before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-07-23 | 2025-10-28 |
| Context window | 128K | — |
| Parameters | 405B | 12B |
| Architecture | decoder only | decoder only |
| License | Open Source | Unknown |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Llama 3.1 405B Instruct | Nemotron-Nano-12B-v2-VL |
|---|---|---|
| Input price | $2.4/1M tokens | $0.2/1M tokens |
| Output price | $2.4/1M tokens | $0.6/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3.1 405B Instruct | Nemotron-Nano-12B-v2-VL |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | Yes |
| 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 and multimodal input: Nemotron-Nano-12B-v2-VL. Both models share structured outputs, 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.
For cost, Llama 3.1 405B Instruct lists $2.4/1M input and $2.4/1M output tokens, while Nemotron-Nano-12B-v2-VL lists $0.2/1M input and $0.6/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Nemotron-Nano-12B-v2-VL lower by about $2.08 per million blended tokens. Availability is 11 providers versus 2, so concentration risk also matters.
Choose Llama 3.1 405B Instruct when provider fit and broader provider choice are central to the workload. Choose Nemotron-Nano-12B-v2-VL when vision-heavy evaluation and lower input-token cost 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
Which is cheaper, Llama 3.1 405B Instruct or Nemotron-Nano-12B-v2-VL?
Nemotron-Nano-12B-v2-VL is cheaper on tracked token pricing. Llama 3.1 405B Instruct costs $2.4/1M input and $2.4/1M output tokens. Nemotron-Nano-12B-v2-VL costs $0.2/1M input and $0.6/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 3.1 405B Instruct or Nemotron-Nano-12B-v2-VL open source?
Llama 3.1 405B Instruct is listed under Open Source. Nemotron-Nano-12B-v2-VL 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.
Which is better for vision, Llama 3.1 405B Instruct or Nemotron-Nano-12B-v2-VL?
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, Llama 3.1 405B Instruct or Nemotron-Nano-12B-v2-VL?
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, Llama 3.1 405B Instruct or Nemotron-Nano-12B-v2-VL?
Both Llama 3.1 405B Instruct and Nemotron-Nano-12B-v2-VL expose structured outputs. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Where can I run Llama 3.1 405B Instruct and Nemotron-Nano-12B-v2-VL?
Llama 3.1 405B Instruct is available on OctoAI API (Deprecated), Together AI, Fireworks AI, IBM watsonx, and Scale AI GenAI Platform. Nemotron-Nano-12B-v2-VL is available on NVIDIA NIM and OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-16. Data sourced from public model cards and provider documentation.