# DeepSeek R1 Distill Qwen 14B vs Kimi K2.5

On provider list prices, DeepSeek R1 Distill Qwen 14B costs $1.60 per million input tokens against $0.50 for Kimi K2.5: effectively level. Output is $1.60 against $2.80 (1.7x). On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | DeepSeek R1 Distill Qwen 14B | Kimi K2.5 |
| --- | --- | --- |
| Lab | DeepSeek | Togethercomputer |
| Access | Open weights | Open weights |
| Context window | 128K tokens | 256K tokens |
| List price, input | $1.60 / M tokens | $0.50 / M tokens |
| List price, output | $1.60 / M tokens | $2.80 / M tokens |
| Cached input | n/a | n/a |
| 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 $1,580 a month on Kimi K2.5 and $2,480 on DeepSeek R1 Distill Qwen 14B at list: a gap of $900, or 1.6x.

Kimi K2.5 reads 256K tokens per request against 128K for DeepSeek R1 Distill Qwen 14B, 2.0x the window. That decides which one can take whole documents without splitting them.

## Choose DeepSeek R1 Distill Qwen 14B for

- Fine-tuning under a permissive license (MIT)

## Choose Kimi K2.5 for

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

## Common questions

### Which is cheaper, DeepSeek R1 Distill Qwen 14B 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 $1.60/$1.60 for DeepSeek R1 Distill Qwen 14B. Billed on Allocate: $0.54/$3.00 against $1.71/$1.71, list plus 7%.

### Which has the bigger context window?

Kimi K2.5: 262,144 tokens (256K) against 131,072 (128K) for DeepSeek R1 Distill Qwen 14B.

### Can I fine-tune DeepSeek R1 Distill Qwen 14B or Kimi K2.5?

Both publish open weights (DeepSeek R1 Distill Qwen 14B: MIT; Kimi K2.5: Not listed), so both can be fine-tuned. On Allocate the trained weights stay inside your boundary and belong to you.

---

[HTML page](https://allocate.network/compare/deepseek-deepseek-r1-distill-qwen-14b-vs-kimi-k2-5) · [DeepSeek R1 Distill Qwen 14B](https://allocate.network/models/deepseek-deepseek-r1-distill-qwen-14b.md) · [Kimi K2.5](https://allocate.network/models/kimi-k2-5.md) · [Machine-readable catalog](https://allocate.network/catalog.json)