Allocate
ModelsSign inTalk to sales
Comparisons

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.

Meta Llama 3.1 8B Instruct Turbo Nvidia Nemotron Nano 9B V2
LabMetaNvidia
AccessOpen weightsOpen weights
Context window128K tokens128K tokens
List price, input$0.18 / M tokens$0.06 / M tokens
List price, output$0.18 / M tokens$0.25 / M tokens
Cached inputn/an/a
LicenseLlama communityCustom license
Fine-tunableYesYes

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.

Nvidia Nemotron Nano 9B V2$0.06$0.25
Meta Llama 3.1 8B Instruct Turbo$0.18$0.18
InputOutput

Choose Meta Llama 3.1 8B Instruct Turbo for

  • Training toward a model you own
Meta Llama 3.1 8B Instruct Turbo details

Choose Nvidia Nemotron Nano 9B V2 for

  • The lower list price ($0.06 in / $0.25 out per M tokens)
Nvidia Nemotron Nano 9B V2 details

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.

Related comparisons

Meta Llama 3.1 8B Instruct Turbo vs Meta Llama 3 8B Instruct ReferenceMeta Llama 3.1 8B Instruct Turbo vs DeepSeek R1 Distill Qwen 1.5BNvidia Nemotron Nano 9B V2 vs DeepSeek R1 Distill Qwen 1.5B

Run the numbers on your workload

LLM price comparisonAgent cost calculatorMeta Llama 3.1 8B Instruct TurboNvidia Nemotron Nano 9B V2

Or don’t choose. On Allocate a route name is the contract: point yours at one model today, swap to the other tomorrow, and compare them on your live traffic with per-token metering.

Route both modelsTalk to sales