# Meta Llama 3.1 405B Instruct vs Llama 4 Scout

On provider list prices, Llama 4 Scout costs $0.18 per million input tokens against $3.50 for Meta Llama 3.1 405B Instruct: 19.4x apart. Output is $0.59 against $3.50 (5.9x). On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Meta Llama 3.1 405B Instruct | Llama 4 Scout |
| --- | --- | --- |
| Lab | Meta | Meta |
| Access | Open weights | Open weights |
| Context window | 4K tokens | 1M tokens |
| List price, input | $3.50 / M tokens | $0.18 / M tokens |
| List price, output | $3.50 / M tokens | $0.59 / 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 $422.50 a month on Llama 4 Scout and $5,425 on Meta Llama 3.1 405B Instruct at list: a gap of $5,003, or 12.8x.

Llama 4 Scout reads 1M tokens per request against 4K for Meta Llama 3.1 405B Instruct, 256.0x the window. That decides which one can take whole documents without splitting them.

## Choose Meta Llama 3.1 405B Instruct for

- Training toward a model you own

## Choose Llama 4 Scout for

- Whole-document reasoning
- High-volume extraction
- Fine-tuning under the Llama 4 license

## Common questions

### Which is cheaper, Meta Llama 3.1 405B Instruct or Llama 4 Scout?

Llama 4 Scout, on this workload shape. At list prices it is $0.18/$0.59 per million tokens in and out against $3.50/$3.50 for Meta Llama 3.1 405B Instruct. Billed on Allocate: $0.19/$0.63 against $3.75/$3.75, list plus 7%.

### Which has the bigger context window?

Llama 4 Scout: 1,048,576 tokens (1M) against 4,096 (4K) for Meta Llama 3.1 405B Instruct.

### Can I fine-tune Meta Llama 3.1 405B Instruct or Llama 4 Scout?

Both publish open weights (Meta Llama 3.1 405B Instruct: Llama community; Llama 4 Scout: 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-llama-3-1-405b-instruct-vs-meta-llama-4-scout-17b-16e-instruct) · [Meta Llama 3.1 405B Instruct](https://allocate.network/models/meta-llama-3-1-405b-instruct.md) · [Llama 4 Scout](https://allocate.network/models/meta-llama-4-scout-17b-16e-instruct.md) · [Machine-readable catalog](https://allocate.network/catalog.json)