Llama 3.1 8B Instruct vs Nemotron-Nano-12B-v2-VL
Llama 3.1 8B Instruct (2024) and Nemotron-Nano-12B-v2-VL (2025) are compact production models from AI at Meta and NVIDIA AI. Llama 3.1 8B Instruct ships a 128K-token context window, while Nemotron-Nano-12B-v2-VL ships a not-yet-sourced context window. On pricing, Llama 3.1 8B Instruct costs $0.02/1M input tokens versus $0.2/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 3.1 8B Instruct is ~900% cheaper at $0.02/1M; pay for Nemotron-Nano-12B-v2-VL only for vision-heavy evaluation.
Decision scorecard
Local evidence first| Signal | Llama 3.1 8B Instruct | Nemotron-Nano-12B-v2-VL |
|---|---|---|
| Decision fit | RAG, Long context, and Classification | Vision and JSON / Tool use |
| Context window | 128K | — |
| Cheapest output | $0.05/1M tokens | $0.6/1M tokens |
| Provider routes | 12 tracked | 2 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 3.1 8B Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 3.1 8B Instruct has the lower cheapest tracked output price at $0.05/1M tokens.
- Llama 3.1 8B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Llama 3.1 8B Instruct for RAG, Long context, and Classification.
- 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 8B Instruct
$28.50
Cheapest tracked route: OpenRouter
Nemotron-Nano-12B-v2-VL
$310
Cheapest tracked route: OpenRouter
Estimated monthly gap: $282. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on NVIDIA NIM and OpenRouter; start route-level A/B tests there.
- Nemotron-Nano-12B-v2-VL is $0.55/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Nemotron-Nano-12B-v2-VL adds Vision and Multimodal in local capability data.
- Provider overlap exists on NVIDIA NIM and OpenRouter; start route-level A/B tests there.
- Llama 3.1 8B Instruct is $0.55/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Vision and Multimodal before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-07-23 | 2025-10-28 |
| Context window | 128K | — |
| Parameters | 8B | 12B |
| Architecture | decoder only | decoder only |
| License | Open Source | Unknown |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Llama 3.1 8B Instruct | Nemotron-Nano-12B-v2-VL |
|---|---|---|
| Input price | $0.02/1M tokens | $0.2/1M tokens |
| Output price | $0.05/1M tokens | $0.6/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3.1 8B 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 8B Instruct lists $0.02/1M input and $0.05/1M 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 Llama 3.1 8B Instruct lower by about $0.29 per million blended tokens. Availability is 12 providers versus 2, so concentration risk also matters.
Choose Llama 3.1 8B Instruct when provider fit, lower input-token cost, and broader provider choice are central to the workload. Choose Nemotron-Nano-12B-v2-VL when vision-heavy evaluation 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 8B Instruct or Nemotron-Nano-12B-v2-VL?
Llama 3.1 8B Instruct is cheaper on tracked token pricing. Llama 3.1 8B Instruct costs $0.02/1M input and $0.05/1M 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 8B Instruct or Nemotron-Nano-12B-v2-VL open source?
Llama 3.1 8B 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 8B 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 8B 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 8B Instruct or Nemotron-Nano-12B-v2-VL?
Both Llama 3.1 8B 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 8B Instruct and Nemotron-Nano-12B-v2-VL?
Llama 3.1 8B Instruct is available on OctoAI API (Deprecated), Together AI, Fireworks AI, NVIDIA NIM, and GroqCloud. 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.