# Kimi K2.5 vs Meta Llama 3.1 405B Instruct

On provider list prices, Kimi K2.5 costs $0.50 per million input tokens against $3.50 for Meta Llama 3.1 405B Instruct: 7.0x apart. Output is $2.80 against $3.50 (1.3x). On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Kimi K2.5 | Meta Llama 3.1 405B Instruct |
| --- | --- | --- |
| Lab | Togethercomputer | Meta |
| Access | Open weights | Open weights |
| Context window | 256K tokens | 4K tokens |
| List price, input | $0.50 / M tokens | $3.50 / M tokens |
| List price, output | $2.80 / M tokens | $3.50 / M tokens |
| Cached input | n/a | n/a |
| License | Not listed | 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 $1,580 a month on Kimi K2.5 and $5,425 on Meta Llama 3.1 405B Instruct at list: a gap of $3,845, or 3.4x.

Kimi K2.5 reads 256K tokens per request against 4K for Meta Llama 3.1 405B Instruct, 64.0x the window. That decides which one can take whole documents without splitting them.

## Choose Kimi K2.5 for

- Whole-document reasoning
- Long-context retrieval
- Open-weight fine-tuning

## Choose Meta Llama 3.1 405B Instruct for

- Training toward a model you own

## Common questions

### Which is cheaper, Kimi K2.5 or Meta Llama 3.1 405B Instruct?

Kimi K2.5, on this workload shape. At list prices it is $0.50/$2.80 per million tokens in and out against $3.50/$3.50 for Meta Llama 3.1 405B Instruct. Billed on Allocate: $0.54/$3.00 against $3.75/$3.75, list plus 7%.

### Which has the bigger context window?

Kimi K2.5: 262,144 tokens (256K) against 4,096 (4K) for Meta Llama 3.1 405B Instruct.

### Can I fine-tune Kimi K2.5 or Meta Llama 3.1 405B Instruct?

Both publish open weights (Kimi K2.5: Not listed; Meta Llama 3.1 405B Instruct: 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/kimi-k2-5-vs-meta-llama-3-1-405b-instruct) · [Kimi K2.5](https://allocate.network/models/kimi-k2-5.md) · [Meta Llama 3.1 405B Instruct](https://allocate.network/models/meta-llama-3-1-405b-instruct.md) · [Machine-readable catalog](https://allocate.network/catalog.json)