# Llama 4 Scout vs Kimi K2.7 Code

On provider list prices, Llama 4 Scout costs $0.18 per million input tokens against $0.95 for Kimi K2.7 Code: 5.3x apart. Output is $0.59 against $4 (6.8x). On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Llama 4 Scout | Kimi K2.7 Code |
| --- | --- | --- |
| Lab | Meta | Moonshot AI |
| Access | Open weights | Open weights |
| Context window | 1M tokens | 256K tokens |
| List price, input | $0.18 / M tokens | $0.95 / M tokens |
| List price, output | $0.59 / M tokens | $4 / M tokens |
| Cached input | n/a | $0.19 / M tokens |
| License | Llama community | Not listed |
| 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 $2,540 on Kimi K2.7 Code at list: a gap of $2,118, or 6.0x.

Llama 4 Scout reads 1M tokens per request against 256K for Kimi K2.7 Code, 4.0x the window. That decides which one can take whole documents without splitting them.

## Choose Llama 4 Scout for

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

## Choose Kimi K2.7 Code for

- Published cached-input pricing ($0.19 per M tokens)

## Common questions

### Which is cheaper, Llama 4 Scout or Kimi K2.7 Code?

Llama 4 Scout, on this workload shape. At list prices it is $0.18/$0.59 per million tokens in and out against $0.95/$4 for Kimi K2.7 Code. Billed on Allocate: $0.19/$0.63 against $1.02/$4.28, list plus 7%.

### Which has the bigger context window?

Llama 4 Scout: 1,048,576 tokens (1M) against 262,144 (256K) for Kimi K2.7 Code.

### Can I fine-tune Llama 4 Scout or Kimi K2.7 Code?

Both publish open weights (Llama 4 Scout: Llama community; Kimi K2.7 Code: Not listed), 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-4-scout-17b-16e-instruct-vs-moonshotai-kimi-k2-7-code) · [Llama 4 Scout](https://allocate.network/models/meta-llama-4-scout-17b-16e-instruct.md) · [Kimi K2.7 Code](https://allocate.network/models/moonshotai-kimi-k2-7-code.md) · [Machine-readable catalog](https://allocate.network/catalog.json)