# Kimi K2.7 Code vs OpenAI GPT-OSS 20B

On provider list prices, OpenAI GPT-OSS 20B costs $0.05 per million input tokens against $0.95 for Kimi K2.7 Code: 19.0x apart. Output is $0.20 against $4 (20.0x). On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Kimi K2.7 Code | OpenAI GPT-OSS 20B |
| --- | --- | --- |
| Lab | Moonshot AI | OpenAI |
| Access | Open weights | Open weights |
| Context window | 256K tokens | 128K tokens |
| List price, input | $0.95 / M tokens | $0.05 / M tokens |
| List price, output | $4 / M tokens | $0.20 / M tokens |
| Cached input | $0.19 / M tokens | n/a |
| License | Not listed | Apache 2.0 |
| 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 $130 a month on OpenAI GPT-OSS 20B and $2,540 on Kimi K2.7 Code at list: a gap of $2,410, or 19.5x.

Kimi K2.7 Code reads 256K tokens per request against 128K for OpenAI GPT-OSS 20B, 2.0x the window. That decides which one can take whole documents without splitting them.

## Choose Kimi K2.7 Code for

- The longer context window (256K vs 128K tokens)
- Published cached-input pricing ($0.19 per M tokens)

## Choose OpenAI GPT-OSS 20B for

- The lower list price ($0.05 in / $0.20 out per M tokens)
- Fine-tuning under a permissive license (Apache 2.0)

## Common questions

### Which is cheaper, Kimi K2.7 Code or OpenAI GPT-OSS 20B?

OpenAI GPT-OSS 20B, on this workload shape. At list prices it is $0.05/$0.20 per million tokens in and out against $0.95/$4 for Kimi K2.7 Code. Billed on Allocate: $0.053/$0.21 against $1.02/$4.28, list plus 7%.

### Which has the bigger context window?

Kimi K2.7 Code: 262,144 tokens (256K) against 131,072 (128K) for OpenAI GPT-OSS 20B.

### Can I fine-tune Kimi K2.7 Code or OpenAI GPT-OSS 20B?

Both publish open weights (Kimi K2.7 Code: Not listed; OpenAI GPT-OSS 20B: Apache 2.0), 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/moonshotai-kimi-k2-7-code-vs-openai-gpt-oss-20b) · [Kimi K2.7 Code](https://allocate.network/models/moonshotai-kimi-k2-7-code.md) · [OpenAI GPT-OSS 20B](https://allocate.network/models/openai-gpt-oss-20b.md) · [Machine-readable catalog](https://allocate.network/catalog.json)