Data story · provider

Moonshot AI: model lineup and pricing overview

Moonshot AI model lineup with prices, context windows and average benchmark scores. Chinese lab behind the Kimi models.

What the data shows

Moonshot AI has 14 models in the index. The average input price is $3.59 and the average output price is $15.38 per million tokens.

The strongest model by average score is kimi-k2. The cheapest by input price is Kimi K1.5 1.5B.

How to read the lineup

  • Start with the best model for quality-sensitive work.
  • Use the cheapest model for high-volume tasks.
  • Check open-weight availability if self-hosting matters.
  • Keep a second provider for fallback.

Moonshot AI models

#ModelInput / 1MOutput / 1MContextAvg score
1Kimi K2$5.85$21.72128,00083.0
2Kimi K2 Instruct$5.80$23.991,000,00082.3
3Kimi K2 Chat$5.74$22.24400,00082.1
4Kimi K1.5$5.19$21.20128,00080.7
5Kimi K1.5 Instruct$5.15$28.20200,00080.5
6Kimi K1.5 Chat$5.11$26.59128,00080.2
7Kimi K2 Base$4.82$16.9065,53679.2
8Kimi K1.5 Base$4.34$15.7065,53677.4
9Kimi K2 8B$1.86$10.038,19264.7
10Kimi K2 7B$1.72$8.9716,38462.7
11Kimi K1.5 8B$1.54$5.988,19262.2
12Kimi K1.5 7B$1.35$4.988,19259.9
13Kimi K2 1.5B$0.99$5.064,09656.8
14Kimi K1.5 1.5B$0.75$3.704,09653.2

Methodology

This story uses the AI & LLM Index dataset: 600 models, 40 providers and 12 benchmarks. Prices are listed per million tokens. Scores are modelled estimates on a 0–100 scale and are not official benchmark results. Figures are rounded for readability.

Related

FAQ

What does the Moonshot AI: model lineup and pricing overview data show?

Moonshot AI model lineup with prices, context windows and average benchmark scores. Chinese lab behind the Kimi models.

Are the numbers official?

No. Prices and scores are compiled for this demo index. Benchmark values are modelled estimates, not official results. Verify figures with the provider or benchmark owner before making decisions.

How should I use this ranking?

Use it to form a shortlist. Check the specific model pages, compare price and context, and test the shortlist on your own examples before committing.