# Kimi K2.5 vs Qwen 3.5

On provider list prices, Kimi K2.5 costs $0.50 per million input tokens against $0.60 for Qwen 3.5: 1.2x apart. Output is $2.80 against $3.60 (1.3x).

## Specifications

| | Kimi K2.5 | Qwen 3.5 |
| --- | --- | --- |
| Lab | Togethercomputer | Qwen |
| Access | Open weights | Open weights |
| Context window | 256K tokens | 256K tokens |
| List price, input | $0.50 / M tokens | $0.60 / M tokens |
| List price, output | $2.80 / M tokens | $3.60 / M tokens |
| Cached input | n/a | $0.35 / M tokens |
| License | Not listed | Apache 2.0 |
| Fine-tunable | Yes | 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 $1,980 on Qwen 3.5 at list: a gap of $400, or 1.3x.

## Choose Kimi K2.5 for

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

## Choose Qwen 3.5 for

- Multilingual support agents
- Translation-adjacent workflows
- Fine-tuning under Apache 2.0

## Common questions

### Which is cheaper, Kimi K2.5 or Qwen 3.5?

Kimi K2.5, on this workload shape. At list prices it is $0.50/$2.80 per million tokens in and out against $0.60/$3.60 for Qwen 3.5. Billed on Allocate: $0.54/$3.00 against $0.64/$3.85.

### Which has the bigger context window?

They match: both read 262,144 tokens (256K) per request.

### Can I fine-tune Kimi K2.5 or Qwen 3.5?

Both publish open weights (Kimi K2.5: Not listed; Qwen 3.5: 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/kimi-k2-5-vs-qwen-3-5) · [Kimi K2.5](https://allocate.network/models/kimi-k2-5.md) · [Qwen 3.5](https://allocate.network/models/qwen-3-5.md) · [Machine-readable catalog](https://allocate.network/catalog.json)