# Meta Llama 3.3 70B Instruct Turbo vs Inkling

On provider list prices, Meta Llama 3.3 70B Instruct Turbo costs $1.04 per million input tokens against $1.87 for Inkling: 1.8x apart. Output is $1.04 against $4.68 (4.5x). On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Meta Llama 3.3 70B Instruct Turbo | Inkling |
| --- | --- | --- |
| Lab | Meta | Thinking Machines |
| Access | Open weights | Open weights |
| Context window | 128K tokens | 1M tokens |
| List price, input | $1.04 / M tokens | $1.87 / M tokens |
| List price, output | $1.04 / M tokens | $4.68 / M tokens |
| Cached input | n/a | $0.37 / M tokens |
| License | Llama community | Apache 2.0 |
| 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 $3,882 on Inkling at list: a gap of $2,270, or 2.4x.

Inkling reads 1M tokens per request against 128K for Meta Llama 3.3 70B Instruct Turbo, 7.6x the window. That decides which one can take whole documents without splitting them.

## Choose Meta Llama 3.3 70B Instruct Turbo for

- The lower list price ($1.04 in / $1.04 out per M tokens)

## Choose Inkling for

- The longer context window (1M vs 128K tokens)
- Fine-tuning under a permissive license (Apache 2.0)
- Published cached-input pricing ($0.37 per M tokens)

## Common questions

### Which is cheaper, Meta Llama 3.3 70B Instruct Turbo or Inkling?

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 $1.87/$4.68 for Inkling. Billed on Allocate: $1.11/$1.11 against $2.00/$5.01, list plus 7%.

### Which has the bigger context window?

Inkling: 1,000,000 tokens (1M) against 131,072 (128K) for Meta Llama 3.3 70B Instruct Turbo.

### Can I fine-tune Meta Llama 3.3 70B Instruct Turbo or Inkling?

Both publish open weights (Meta Llama 3.3 70B Instruct Turbo: Llama community; Inkling: Apache 2.0), 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-3-3-70b-instruct-turbo-vs-thinkingmachines-inkling) · [Meta Llama 3.3 70B Instruct Turbo](https://allocate.network/models/meta-llama-3-3-70b-instruct-turbo.md) · [Inkling](https://allocate.network/models/thinkingmachines-inkling.md) · [Machine-readable catalog](https://allocate.network/catalog.json)