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

On provider list prices, Meta Llama 3.1 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 3.1 70B Instruct Turbo | Llama 3.1 Nemotron 70B Instruct HF |
| --- | --- | --- |
| Lab | Meta | nvidia |
| Access | Open weights | Open weights |
| Context window | 128K 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 3.1 70B Instruct Turbo and $1,364 on Llama 3.1 Nemotron 70B Instruct HF at list: a gap of $0.

Meta Llama 3.1 70B Instruct Turbo reads 128K tokens per request against 32K for Llama 3.1 Nemotron 70B Instruct HF, 4.0x the window. That decides which one can take whole documents without splitting them.

## Choose Meta Llama 3.1 70B Instruct Turbo for

- The longer context window (128K vs 32K tokens)

## Choose Llama 3.1 Nemotron 70B Instruct HF for

- Training toward a model you own

## Common questions

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

Meta Llama 3.1 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?

Meta Llama 3.1 70B Instruct Turbo: 131,072 tokens (128K) against 32,768 (32K) for Llama 3.1 Nemotron 70B Instruct HF.

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

Both publish open weights (Meta Llama 3.1 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.

---

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