# Meta Llama 3 70B Instruct Turbo vs Llama 3.1 Nemotron 70B Instruct HF

On provider list prices, Meta Llama 3 70B Instruct Turbo costs $0.88 per million input tokens against $0.88 for Llama 3.1 Nemotron 70B Instruct HF: effectively level. Output is $0.88 against $0.88. On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Meta Llama 3 70B Instruct Turbo | Llama 3.1 Nemotron 70B Instruct HF |
| --- | --- | --- |
| Lab | Meta | nvidia |
| Access | Open weights | Open weights |
| Context window | 8K tokens | 32K tokens |
| List price, input | $0.88 / M tokens | $0.88 / M tokens |
| List price, output | $0.88 / M tokens | $0.88 / M tokens |
| Cached input | n/a | n/a |
| License | Llama community | Llama community |
| Fine-tunable | Yes | Yes |

Specifications and provider list prices from the Allocate catalog, checked 2026-07-08. 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 $1,364 a month on Meta Llama 3 70B Instruct Turbo and $1,364 on Llama 3.1 Nemotron 70B Instruct HF at list: a gap of $0.

Llama 3.1 Nemotron 70B Instruct HF reads 32K tokens per request against 8K for Meta Llama 3 70B Instruct Turbo, 4.0x the window. That decides which one can take whole documents without splitting them.

## Choose Meta Llama 3 70B Instruct Turbo for

- Training toward a model you own

## Choose Llama 3.1 Nemotron 70B Instruct HF for

- The longer context window (32K vs 8K tokens)

## Common questions

### Which is cheaper, Meta Llama 3 70B Instruct Turbo or Llama 3.1 Nemotron 70B Instruct HF?

Meta Llama 3 70B Instruct Turbo, on this workload shape. At list prices it is $0.88/$0.88 per million tokens in and out against $0.88/$0.88 for Llama 3.1 Nemotron 70B Instruct HF. Billed on Allocate: $0.94/$0.94 against $0.94/$0.94, list plus 7%.

### Which has the bigger context window?

Llama 3.1 Nemotron 70B Instruct HF: 32,768 tokens (32K) against 8,192 (8K) for Meta Llama 3 70B Instruct Turbo.

### Can I fine-tune Meta Llama 3 70B Instruct Turbo or Llama 3.1 Nemotron 70B Instruct HF?

Both publish open weights (Meta Llama 3 70B Instruct Turbo: Llama community; Llama 3.1 Nemotron 70B Instruct HF: Llama community), so both can be fine-tuned. On Allocate the trained weights stay inside your boundary and belong to you.

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[HTML page](https://allocate.network/compare/meta-meta-llama-3-70b-instruct-turbo-vs-nvidia-llama-3-1-nemotron-70b-instruct-hf) · [Meta Llama 3 70B Instruct Turbo](https://allocate.network/models/meta-meta-llama-3-70b-instruct-turbo.md) · [Llama 3.1 Nemotron 70B Instruct HF](https://allocate.network/models/nvidia-llama-3-1-nemotron-70b-instruct-hf.md) · [Machine-readable catalog](https://allocate.network/catalog.json)