Kimi K3 pricing for sentiment analysis

Quick start

Call Kimi K3 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-k3",
    max_tokens=100,
    messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.003000 per call at standard rates
# Input:  $3.00/MTok
# Output: $15.00/MTok

How Kimi K3 compares on cost

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

Cheapest viable option: GPT 5.6 Luna at $0.000220/call (93% less, 91/100 quality).

What drives sentiment analysis cost

Short text in, a label or score out: the cheapest shape an API call can take. Output pricing is irrelevant at this size. Because these run in bulk over historical data as often as in real time, the batch API discount typically applies, and the effective rate lands well below list.

For Kimi K3 specifically, that means output tokens cost 5.0x more than input, at $15.00 vs $3.00 per MTok. At this use case’s 500/100 token split, output accounts for 50% of the $0.003000 per-call cost.

Kimi K3 quality benchmarks for sentiment analysis

Benchmark data for sentiment analysis is pending for Kimi K3. Test against your own evaluation suite before moving to production.

Benchmark data is not yet available for Kimi K3. We recommend testing against your own evaluation criteria.

About Kimi K3

Prices from platform.kimi.ai (read 2026-10-11). Cache hit: $0.30/MTok input. Kimi K3 is a 2.8T-parameter open-weight MoE model. No batch discount published yet. Not yet benchmarked, so never recommended.

Other models from Moonshot (Kimi)

You can compare Kimi K3 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