# Meta Llama 3.3 70B Instruct Turbo vs Kimi K2.7 Code

On provider list prices, Meta Llama 3.3 70B Instruct Turbo costs $1.04 per million input tokens against $0.95 for Kimi K2.7 Code: effectively level. Output is $1.04 against $4 (3.8x). On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Meta Llama 3.3 70B Instruct Turbo | Kimi K2.7 Code |
| --- | --- | --- |
| Lab | Meta | Moonshot AI |
| Access | Open weights | Open weights |
| Context window | 128K tokens | 256K tokens |
| List price, input | $1.04 / M tokens | $0.95 / M tokens |
| List price, output | $1.04 / 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 $1,612 a month on Meta Llama 3.3 70B Instruct Turbo and $2,540 on Kimi K2.7 Code at list: a gap of $928, or 1.6x.

Kimi K2.7 Code 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 Meta Llama 3.3 70B Instruct Turbo for

- Training toward a model you own

## Choose Kimi K2.7 Code for

- The lower list price ($0.95 in / $4 out per M tokens)
- The longer context window (256K vs 128K tokens)
- Published cached-input pricing ($0.19 per M tokens)

## Common questions

### Which is cheaper, Meta Llama 3.3 70B Instruct Turbo or Kimi K2.7 Code?

Meta Llama 3.3 70B Instruct Turbo, on this workload shape. At list prices it is $1.04/$1.04 per million tokens in and out against $0.95/$4 for Kimi K2.7 Code. Billed on Allocate: $1.11/$1.11 against $1.02/$4.28, list plus 7%.

### Which has the bigger context window?

Kimi K2.7 Code: 262,144 tokens (256K) against 131,072 (128K) for Meta Llama 3.3 70B Instruct Turbo.

### Can I fine-tune Meta Llama 3.3 70B Instruct Turbo or Kimi K2.7 Code?

Both publish open weights (Meta Llama 3.3 70B Instruct Turbo: 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.

---

[HTML page](https://allocate.network/compare/meta-llama-3-3-70b-instruct-turbo-vs-moonshotai-kimi-k2-7-code) · [Meta Llama 3.3 70B Instruct Turbo](https://allocate.network/models/meta-llama-3-3-70b-instruct-turbo.md) · [Kimi K2.7 Code](https://allocate.network/models/moonshotai-kimi-k2-7-code.md) · [Machine-readable catalog](https://allocate.network/catalog.json)