Llama 3.1 8B Instruct vs Nemotron-Nano-9B-v2
Llama 3.1 8B Instruct (2024) and Nemotron-Nano-9B-v2 (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-9B-v2 ships a not-yet-sourced context window. On pricing, Llama 3.1 8B Instruct costs $0.02/1M input tokens versus $0.04/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 3.1 8B Instruct is ~100% cheaper at $0.02/1M; pay for Nemotron-Nano-9B-v2 only for provider fit.
Decision scorecard
Local evidence first| Signal | Llama 3.1 8B Instruct | Nemotron-Nano-9B-v2 |
|---|---|---|
| Decision fit | RAG, Long context, and Classification | Classification and JSON / Tool use |
| Context window | 128K | — |
| Cheapest output | $0.05/1M tokens | $0.16/1M 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.
- Local decision data tags Nemotron-Nano-9B-v2 for Classification 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-9B-v2
$72.00
Cheapest tracked route: OpenRouter
Estimated monthly gap: $43.50. 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-9B-v2 is $0.11/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Provider overlap exists on NVIDIA NIM and OpenRouter; start route-level A/B tests there.
- Llama 3.1 8B Instruct is $0.11/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-07-23 | 2025-08-18 |
| Context window | 128K | — |
| Parameters | 8B | 9B |
| Architecture | decoder only | decoder only |
| License | Open Source | Unknown |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Llama 3.1 8B Instruct | Nemotron-Nano-9B-v2 |
|---|---|---|
| Input price | $0.02/1M tokens | $0.04/1M tokens |
| Output price | $0.05/1M tokens | $0.16/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3.1 8B Instruct | Nemotron-Nano-9B-v2 |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| 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 is close: both models cover structured outputs. 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.
For cost, Llama 3.1 8B Instruct lists $0.02/1M input and $0.05/1M output tokens, while Nemotron-Nano-9B-v2 lists $0.04/1M input and $0.16/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.1 8B Instruct lower by about $0.05 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-9B-v2 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
Which is cheaper, Llama 3.1 8B Instruct or Nemotron-Nano-9B-v2?
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-9B-v2 costs $0.04/1M input and $0.16/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 3.1 8B Instruct or Nemotron-Nano-9B-v2 open source?
Llama 3.1 8B Instruct is listed under Open Source. Nemotron-Nano-9B-v2 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 structured outputs, Llama 3.1 8B Instruct or Nemotron-Nano-9B-v2?
Both Llama 3.1 8B Instruct and Nemotron-Nano-9B-v2 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-9B-v2?
Llama 3.1 8B Instruct is available on OctoAI API (Deprecated), Together AI, Fireworks AI, NVIDIA NIM, and GroqCloud. Nemotron-Nano-9B-v2 is available on NVIDIA NIM and OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.1 8B Instruct over Nemotron-Nano-9B-v2?
Llama 3.1 8B Instruct is ~100% cheaper at $0.02/1M; pay for Nemotron-Nano-9B-v2 only for provider fit. If your workload also depends on provider fit, start with Llama 3.1 8B Instruct; if it depends on provider fit, run the same evaluation with Nemotron-Nano-9B-v2.
Continue comparing
Last reviewed: 2026-05-16. Data sourced from public model cards and provider documentation.