# DeepSeek R1 0528 NVFP4 vs Inkling

On provider list prices, Inkling costs $1.87 per million input tokens against $3 for DeepSeek R1 0528 NVFP4: 1.6x apart. Output is $4.68 against $7 (1.5x). On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | DeepSeek R1 0528 NVFP4 | Inkling |
| --- | --- | --- |
| Lab | Deepseek | Thinking Machines |
| Access | Open weights | Open weights |
| Context window | 160K tokens | 1M tokens |
| List price, input | $3 / M tokens | $1.87 / M tokens |
| List price, output | $7 / M tokens | $4.68 / M tokens |
| Cached input | n/a | $0.37 / M tokens |
| License | MIT | 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 $6,050 on DeepSeek R1 0528 NVFP4 at list: a gap of $2,168, or 1.6x.

Inkling reads 1M tokens per request against 160K for DeepSeek R1 0528 NVFP4, 6.1x the window. That decides which one can take whole documents without splitting them.

## Choose DeepSeek R1 0528 NVFP4 for

- Fine-tuning under a permissive license (MIT)

## Choose Inkling for

- The lower list price ($1.87 in / $4.68 out per M tokens)
- The longer context window (1M vs 160K tokens)
- Fine-tuning under a permissive license (Apache 2.0)

## Common questions

### Which is cheaper, DeepSeek R1 0528 NVFP4 or Inkling?

Inkling, on this workload shape. At list prices it is $1.87/$4.68 per million tokens in and out against $3/$7 for DeepSeek R1 0528 NVFP4. Billed on Allocate: $2.00/$5.01 against $3.21/$7.49, list plus 7%.

### Which has the bigger context window?

Inkling: 1,000,000 tokens (1M) against 163,840 (160K) for DeepSeek R1 0528 NVFP4.

### Can I fine-tune DeepSeek R1 0528 NVFP4 or Inkling?

Both publish open weights (DeepSeek R1 0528 NVFP4: MIT; 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/deepseek-deepseek-r1-0528-vs-thinkingmachines-inkling) · [DeepSeek R1 0528 NVFP4](https://allocate.network/models/deepseek-deepseek-r1-0528.md) · [Inkling](https://allocate.network/models/thinkingmachines-inkling.md) · [Machine-readable catalog](https://allocate.network/catalog.json)