# DeepSeek R1 0528 NVFP4 vs Kimi K2.7 Code

On provider list prices, Kimi K2.7 Code costs $0.95 per million input tokens against $3 for DeepSeek R1 0528 NVFP4: 3.2x apart. Output is $4 against $7 (1.8x). On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | DeepSeek R1 0528 NVFP4 | Kimi K2.7 Code |
| --- | --- | --- |
| Lab | Deepseek | Moonshot AI |
| Access | Open weights | Open weights |
| Context window | 160K tokens | 256K tokens |
| List price, input | $3 / M tokens | $0.95 / M tokens |
| List price, output | $7 / M tokens | $4 / M tokens |
| Cached input | n/a | $0.19 / M tokens |
| License | MIT | 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 $2,540 a month on Kimi K2.7 Code and $6,050 on DeepSeek R1 0528 NVFP4 at list: a gap of $3,510, or 2.4x.

Kimi K2.7 Code reads 256K tokens per request against 160K for DeepSeek R1 0528 NVFP4, 1.6x 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 Kimi K2.7 Code for

- The lower list price ($0.95 in / $4 out per M tokens)
- The longer context window (256K vs 160K tokens)
- Published cached-input pricing ($0.19 per M tokens)

## Common questions

### Which is cheaper, DeepSeek R1 0528 NVFP4 or Kimi K2.7 Code?

Kimi K2.7 Code, on this workload shape. At list prices it is $0.95/$4 per million tokens in and out against $3/$7 for DeepSeek R1 0528 NVFP4. Billed on Allocate: $1.02/$4.28 against $3.21/$7.49, list plus 7%.

### Which has the bigger context window?

Kimi K2.7 Code: 262,144 tokens (256K) against 163,840 (160K) for DeepSeek R1 0528 NVFP4.

### Can I fine-tune DeepSeek R1 0528 NVFP4 or Kimi K2.7 Code?

Both publish open weights (DeepSeek R1 0528 NVFP4: MIT; 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.

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[HTML page](https://allocate.network/compare/deepseek-deepseek-r1-0528-vs-moonshotai-kimi-k2-7-code) · [DeepSeek R1 0528 NVFP4](https://allocate.network/models/deepseek-deepseek-r1-0528.md) · [Kimi K2.7 Code](https://allocate.network/models/moonshotai-kimi-k2-7-code.md) · [Machine-readable catalog](https://allocate.network/catalog.json)