# Kimi K2.7 Code vs OpenAI GPT-OSS 120B

On provider list prices, OpenAI GPT-OSS 120B costs $0.15 per million input tokens against $0.95 for Kimi K2.7 Code: 6.3x apart. Output is $0.60 against $4 (6.7x). On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Kimi K2.7 Code | OpenAI GPT-OSS 120B |
| --- | --- | --- |
| Lab | Moonshot AI | OpenAI |
| Access | Open weights | Open weights |
| Context window | 256K tokens | 128K tokens |
| List price, input | $0.95 / M tokens | $0.15 / M tokens |
| List price, output | $4 / M tokens | $0.60 / M tokens |
| Cached input | $0.19 / M tokens | n/a |
| License | Not listed | Custom license |
| 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 $390 a month on OpenAI GPT-OSS 120B and $2,540 on Kimi K2.7 Code at list: a gap of $2,150, or 6.5x.

Kimi K2.7 Code reads 256K tokens per request against 128K for OpenAI GPT-OSS 120B, 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 120B for

- The lower list price ($0.15 in / $0.60 out per M tokens)

## Common questions

### Which is cheaper, Kimi K2.7 Code or OpenAI GPT-OSS 120B?

OpenAI GPT-OSS 120B, on this workload shape. At list prices it is $0.15/$0.60 per million tokens in and out against $0.95/$4 for Kimi K2.7 Code. Billed on Allocate: $0.16/$0.64 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 120B.

### Can I fine-tune Kimi K2.7 Code or OpenAI GPT-OSS 120B?

Both publish open weights (Kimi K2.7 Code: Not listed; OpenAI GPT-OSS 120B: Custom license), 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/moonshotai-kimi-k2-7-code-vs-openai-gpt-oss-120b) · [Kimi K2.7 Code](https://allocate.network/models/moonshotai-kimi-k2-7-code.md) · [OpenAI GPT-OSS 120B](https://allocate.network/models/openai-gpt-oss-120b.md) · [Machine-readable catalog](https://allocate.network/catalog.json)