# Meta Llama 3.1 70B Instruct Turbo vs Qwen2 72B Instruct

On provider list prices, Meta Llama 3.1 70B Instruct Turbo costs $0.88 per million input tokens against $0.90 for Qwen2 72B Instruct: effectively level. Output is $0.88 against $0.90. On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Meta Llama 3.1 70B Instruct Turbo | Qwen2 72B Instruct |
| --- | --- | --- |
| Lab | Meta | Togethercomputer |
| Access | Open weights | Open weights |
| Context window | 128K tokens | 32K tokens |
| List price, input | $0.88 / M tokens | $0.90 / M tokens |
| List price, output | $0.88 / M tokens | $0.90 / M tokens |
| Cached input | n/a | n/a |
| License | Llama community | Qwen license |
| 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 $1,395 on Qwen2 72B Instruct at list: a gap of $31.

Meta Llama 3.1 70B Instruct Turbo reads 128K tokens per request against 32K for Qwen2 72B Instruct, 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 lower list price ($0.88 in / $0.88 out per M tokens)
- The longer context window (128K vs 32K tokens)

## Choose Qwen2 72B Instruct for

- Training toward a model you own

## Common questions

### Which is cheaper, Meta Llama 3.1 70B Instruct Turbo or Qwen2 72B Instruct?

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 $0.90/$0.90 for Qwen2 72B Instruct. Billed on Allocate: $0.94/$0.94 against $0.96/$0.96, 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 Qwen2 72B Instruct.

### Can I fine-tune Meta Llama 3.1 70B Instruct Turbo or Qwen2 72B Instruct?

Both publish open weights (Meta Llama 3.1 70B Instruct Turbo: Llama community; Qwen2 72B Instruct: Qwen license), 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-3-1-70b-instruct-turbo-vs-qwen-qwen2-72b-instruct) · [Meta Llama 3.1 70B Instruct Turbo](https://allocate.network/models/meta-meta-llama-3-1-70b-instruct-turbo.md) · [Qwen2 72B Instruct](https://allocate.network/models/qwen-qwen2-72b-instruct.md) · [Machine-readable catalog](https://allocate.network/catalog.json)