# Kimi K2.5 vs Meta Llama 3.3 70B Instruct Turbo

On provider list prices, Meta Llama 3.3 70B Instruct Turbo costs $1.04 per million input tokens against $0.50 for Kimi K2.5: effectively level. Output is $1.04 against $2.80 (2.7x). On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Kimi K2.5 | Meta Llama 3.3 70B Instruct Turbo |
| --- | --- | --- |
| Lab | Togethercomputer | Meta |
| Access | Open weights | Open weights |
| Context window | 256K tokens | 128K tokens |
| List price, input | $0.50 / M tokens | $1.04 / M tokens |
| List price, output | $2.80 / M tokens | $1.04 / 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 $1,612 on Meta Llama 3.3 70B Instruct Turbo at list: a gap of $32.

Kimi K2.5 reads 256K tokens per request against 128K for Meta Llama 3.3 70B Instruct Turbo, 2.0x 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 3.3 70B Instruct Turbo for

- Training toward a model you own

## Common questions

### Which is cheaper, Kimi K2.5 or Meta Llama 3.3 70B Instruct Turbo?

Kimi K2.5, on this workload shape. At list prices it is $0.50/$2.80 per million tokens in and out against $1.04/$1.04 for Meta Llama 3.3 70B Instruct Turbo. Billed on Allocate: $0.54/$3.00 against $1.11/$1.11, list plus 7%.

### Which has the bigger context window?

Kimi K2.5: 262,144 tokens (256K) against 131,072 (128K) for Meta Llama 3.3 70B Instruct Turbo.

### Can I fine-tune Kimi K2.5 or Meta Llama 3.3 70B Instruct Turbo?

Both publish open weights (Kimi K2.5: Not listed; Meta Llama 3.3 70B Instruct Turbo: 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-3-3-70b-instruct-turbo) · [Kimi K2.5](https://allocate.network/models/kimi-k2-5.md) · [Meta Llama 3.3 70B Instruct Turbo](https://allocate.network/models/meta-llama-3-3-70b-instruct-turbo.md) · [Machine-readable catalog](https://allocate.network/catalog.json)