Kimi K2 pricing for categorization

Quick start

Call Kimi K2 with the OpenAI Python SDK pointed at api.moonshot.cn:

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.moonshot.cn/v1",
    api_key=os.environ["MOONSHOT_API_KEY"],
)
response = client.chat.completions.create(
    model="kimi-k2",
    max_tokens=150,
    messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.001360 per call at standard rates
# Input:  $0.95/MTok
# Output: $4.00/MTok

How Kimi K2 compares on cost

Per-call cost for all models scoring 50+ on categorization benchmarks. Hover for details.

Cheapest viable option: GPT 5.6 Luna at $0.000340/call (75% less, 91/100 quality).

What drives categorization cost

Classification into a fixed taxonomy is token-efficient by construction: the taxonomy is the same on every call and the output is one label. Put the taxonomy in the cached prefix and the marginal cost per item approaches the cost of the item text alone. Model tier matters less here than in any other use case tracked.

For Kimi K2 specifically, that means output tokens cost 4.2x more than input, at $4.00 vs $0.95 per MTok. At this use case’s 800/150 token split, output accounts for 44% of the $0.001360 per-call cost.

Kimi K2 quality benchmarks for categorization

Benchmark data for categorization is pending for Kimi K2. Test against your own evaluation suite before moving to production.

Benchmark data is not yet available for Kimi K2. We recommend testing against your own evaluation criteria.

About Kimi K2

Prices from platform.kimi.ai (K2.6, read 2026-10-11). Cache hit: $0.16/MTok input. Batch API at 60% of standard rate. Not yet benchmarked, so never recommended.

Other models from Moonshot (Kimi)

You can compare Kimi K2 head-to-head against any other model on our model comparison pages, or track how pricing has changed over time on the pricing history tracker.

Frequently asked questions