# DeepSeek V4 vs Kimi K2.5

On provider list prices, Kimi K2.5 costs $0.50 per million input tokens against $1.74 for DeepSeek V4: 3.5x apart. Output is $2.80 against $3.48 (1.2x).

## Specifications

| | DeepSeek V4 | Kimi K2.5 |
| --- | --- | --- |
| Lab | Deepseek | Togethercomputer |
| Access | API only | Open weights |
| Context window | 512K tokens | 256K tokens |
| List price, input | $1.74 / M tokens | $0.50 / M tokens |
| List price, output | $3.48 / M tokens | $2.80 / M tokens |
| Cached input | $0.20 / M tokens | n/a |
| License | Proprietary API | Not listed |
| Fine-tunable | No | Yes |

Specifications and provider list prices from the Allocate catalog, checked 2026-07-21.

## What the numbers say

Take 1,000,000 requests a month at 1,200 input and 350 output tokens each. That workload costs $1,580 a month on Kimi K2.5 and $3,306 on DeepSeek V4 at list: a gap of $1,726, or 2.1x.

DeepSeek V4 reads 512K tokens per request against 256K for Kimi K2.5, 2.0x the window. That decides which one can take whole documents without splitting them.

## Choose DeepSeek V4 for

- Reasoning-heavy agents
- Long-document analysis
- Cost-sensitive production routes

## Choose Kimi K2.5 for

- Whole-document reasoning
- Long-context retrieval
- Open-weight fine-tuning

## Common questions

### Which is cheaper, DeepSeek V4 or Kimi K2.5?

Kimi K2.5, on this workload shape. At list prices it is $0.50/$2.80 per million tokens in and out against $1.74/$3.48 for DeepSeek V4. Billed on Allocate: $0.54/$3.00 against $1.86/$3.72.

### Which has the bigger context window?

DeepSeek V4: 512,000 tokens (512K) against 262,144 (256K) for Kimi K2.5.

### Can I fine-tune DeepSeek V4 or Kimi K2.5?

Kimi K2.5 publishes open weights (Not listed) and can be fine-tuned on your own data. DeepSeek V4 is a closed model served over API; its weights are not available.

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[HTML page](https://allocate.network/compare/deepseek-v4-vs-kimi-k2-5) · [DeepSeek V4](https://allocate.network/models/deepseek-v4.md) · [Kimi K2.5](https://allocate.network/models/kimi-k2-5.md) · [Machine-readable catalog](https://allocate.network/catalog.json)