# Meta Llama 3.1 8B Instruct Turbo vs Nvidia Nemotron Nano 9B V2

On provider list prices, Nvidia Nemotron Nano 9B V2 costs $0.06 per million input tokens against $0.18 for Meta Llama 3.1 8B Instruct Turbo: 3.0x apart. Output is $0.25 against $0.18. On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Meta Llama 3.1 8B Instruct Turbo | Nvidia Nemotron Nano 9B V2 |
| --- | --- | --- |
| Lab | Meta | Nvidia |
| Access | Open weights | Open weights |
| Context window | 128K tokens | 128K tokens |
| List price, input | $0.18 / M tokens | $0.06 / M tokens |
| List price, output | $0.18 / M tokens | $0.25 / M tokens |
| Cached input | n/a | n/a |
| License | Llama community | Custom license |
| Fine-tunable | Yes | Yes |

Specifications and provider list prices from the Allocate catalog, checked 2026-07-21. Billed price is list plus the 7% transaction fee.

## What the numbers say

Take 1,000,000 requests a month at 1,200 input and 350 output tokens each. That workload costs $159.50 a month on Nvidia Nemotron Nano 9B V2 and $279 on Meta Llama 3.1 8B Instruct Turbo at list: a gap of $119.50, or 1.7x.

## Choose Meta Llama 3.1 8B Instruct Turbo for

- Training toward a model you own

## Choose Nvidia Nemotron Nano 9B V2 for

- The lower list price ($0.06 in / $0.25 out per M tokens)

## Common questions

### Which is cheaper, Meta Llama 3.1 8B Instruct Turbo or Nvidia Nemotron Nano 9B V2?

Nvidia Nemotron Nano 9B V2, on this workload shape. At list prices it is $0.06/$0.25 per million tokens in and out against $0.18/$0.18 for Meta Llama 3.1 8B Instruct Turbo. Billed on Allocate: $0.064/$0.27 against $0.19/$0.19, list plus 7%.

### Which has the bigger context window?

They match: both read 131,072 tokens (128K) per request.

### Can I fine-tune Meta Llama 3.1 8B Instruct Turbo or Nvidia Nemotron Nano 9B V2?

Both publish open weights (Meta Llama 3.1 8B Instruct Turbo: Llama community; Nvidia Nemotron Nano 9B V2: Custom license), so both can be fine-tuned. On Allocate the trained weights stay inside your boundary and belong to you.

---

[HTML page](https://allocate.network/compare/meta-meta-llama-3-1-8b-instruct-turbo-vs-nvidia-nvidia-nemotron-nano-9b-v2) · [Meta Llama 3.1 8B Instruct Turbo](https://allocate.network/models/meta-meta-llama-3-1-8b-instruct-turbo.md) · [Nvidia Nemotron Nano 9B V2](https://allocate.network/models/nvidia-nvidia-nemotron-nano-9b-v2.md) · [Machine-readable catalog](https://allocate.network/catalog.json)