# Meta Llama 3.1 405B Instruct vs Inkling

On provider list prices, Inkling costs $1.87 per million input tokens against $3.50 for Meta Llama 3.1 405B Instruct: 1.9x apart. Output is $4.68 against $3.50. On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Meta Llama 3.1 405B Instruct | Inkling |
| --- | --- | --- |
| Lab | Meta | Thinking Machines |
| Access | Open weights | Open weights |
| Context window | 4K tokens | 1M tokens |
| List price, input | $3.50 / M tokens | $1.87 / M tokens |
| List price, output | $3.50 / 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 $3,882 a month on Inkling and $5,425 on Meta Llama 3.1 405B Instruct at list: a gap of $1,543, or 1.4x.

Inkling reads 1M tokens per request against 4K for Meta Llama 3.1 405B Instruct, 244.1x the window. That decides which one can take whole documents without splitting them.

## Choose Meta Llama 3.1 405B Instruct for

- Training toward a model you own

## Choose Inkling for

- The lower list price ($1.87 in / $4.68 out per M tokens)
- The longer context window (1M vs 4K tokens)
- Fine-tuning under a permissive license (Apache 2.0)

## Common questions

### Which is cheaper, Meta Llama 3.1 405B Instruct or Inkling?

Inkling, on this workload shape. At list prices it is $1.87/$4.68 per million tokens in and out against $3.50/$3.50 for Meta Llama 3.1 405B Instruct. Billed on Allocate: $2.00/$5.01 against $3.75/$3.75, list plus 7%.

### Which has the bigger context window?

Inkling: 1,000,000 tokens (1M) against 4,096 (4K) for Meta Llama 3.1 405B Instruct.

### Can I fine-tune Meta Llama 3.1 405B Instruct or Inkling?

Both publish open weights (Meta Llama 3.1 405B Instruct: 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-3-1-405b-instruct-vs-thinkingmachines-inkling) · [Meta Llama 3.1 405B Instruct](https://allocate.network/models/meta-llama-3-1-405b-instruct.md) · [Inkling](https://allocate.network/models/thinkingmachines-inkling.md) · [Machine-readable catalog](https://allocate.network/catalog.json)