# Meta Llama 3 70B Instruct Turbo vs Qwen2 72B Instruct

On provider list prices, Meta Llama 3 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 70B Instruct Turbo | Qwen2 72B Instruct |
| --- | --- | --- |
| Lab | Meta | Togethercomputer |
| Access | Open weights | Open weights |
| Context window | 8K 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 70B Instruct Turbo and $1,395 on Qwen2 72B Instruct at list: a gap of $31.

Qwen2 72B Instruct 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

- The lower list price ($0.88 in / $0.88 out per M tokens)

## Choose Qwen2 72B Instruct for

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

## Common questions

### Which is cheaper, Meta Llama 3 70B Instruct Turbo or Qwen2 72B Instruct?

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 $0.90/$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?

Qwen2 72B Instruct: 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 Qwen2 72B Instruct?

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