# DeepSeek R1 0528 NVFP4 vs Deepseek V3.1 NVFP4

On provider list prices, Deepseek V3.1 NVFP4 costs $0.60 per million input tokens against $3 for DeepSeek R1 0528 NVFP4: 5.0x apart. Output is $1.70 against $7 (4.1x). On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | DeepSeek R1 0528 NVFP4 | Deepseek V3.1 NVFP4 |
| --- | --- | --- |
| Lab | Deepseek | DeepSeek |
| Access | Open weights | Open weights |
| Context window | 160K tokens | 128K tokens |
| List price, input | $3 / M tokens | $0.60 / M tokens |
| List price, output | $7 / M tokens | $1.70 / M tokens |
| Cached input | n/a | n/a |
| License | MIT | MIT |
| 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 $1,315 a month on Deepseek V3.1 NVFP4 and $6,050 on DeepSeek R1 0528 NVFP4 at list: a gap of $4,735, or 4.6x.

DeepSeek R1 0528 NVFP4 reads 160K tokens per request against 128K for Deepseek V3.1 NVFP4, 1.3x the window. That decides which one can take whole documents without splitting them.

## Choose DeepSeek R1 0528 NVFP4 for

- The longer context window (160K vs 128K tokens)
- Fine-tuning under a permissive license (MIT)

## Choose Deepseek V3.1 NVFP4 for

- The lower list price ($0.60 in / $1.70 out per M tokens)
- Fine-tuning under a permissive license (MIT)

## Common questions

### Which is cheaper, DeepSeek R1 0528 NVFP4 or Deepseek V3.1 NVFP4?

Deepseek V3.1 NVFP4, on this workload shape. At list prices it is $0.60/$1.70 per million tokens in and out against $3/$7 for DeepSeek R1 0528 NVFP4. Billed on Allocate: $0.64/$1.82 against $3.21/$7.49, list plus 7%.

### Which has the bigger context window?

DeepSeek R1 0528 NVFP4: 163,840 tokens (160K) against 131,072 (128K) for Deepseek V3.1 NVFP4.

### Can I fine-tune DeepSeek R1 0528 NVFP4 or Deepseek V3.1 NVFP4?

Both publish open weights (DeepSeek R1 0528 NVFP4: MIT; Deepseek V3.1 NVFP4: MIT), 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-deepseek-deepseek-v3-1) · [DeepSeek R1 0528 NVFP4](https://allocate.network/models/deepseek-deepseek-r1-0528.md) · [Deepseek V3.1 NVFP4](https://allocate.network/models/deepseek-deepseek-v3-1.md) · [Machine-readable catalog](https://allocate.network/catalog.json)