Moonshot (Kimi) · Mid-tier · 256K context window

Kimi K2 pricing for email summarization

$0.004850 per call at 3,000 input / 500 output tokens.

Per call

$0.004850

10K calls/mo

$48.5

Cache savings

83% off input

Batch savings

40% off all

Calculate your Kimi K2 costs

Adjust token counts, volume, and discount toggles to model your email summarization spend. Prices verified daily against provider pricing pages.

Configure your usage

3,000
100100K
500
5050K
10,000
1001M

Estimated cost

Monthly cost
$49
10,000 calls/month
Cost per call$0.004850
Daily cost$2
Input rate$0.95/MTok
Output rate$4.00/MTok
Analyze your real spend

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=500,
    messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.004850 per call at standard rates
# Input:  $0.95/MTok
# Output: $4.00/MTok

What drives email summarization cost

Individually cheap, collectively expensive. A single thread is small, but summarization runs across an inbox at volume, so per-call price multiplied by message count is the number that matters, not the cost of any one summary. This is the use case where a fast-tier model and batch processing usually beat a smarter model outright.

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 3,000/500 token split, output accounts for 41% of the $0.004850 per-call cost.

Complete Kimi K2 pricing breakdown

Standard token rates

Direction$/MTokTokens per call
Input$0.953,000
Output$4.00500

Prompt caching: 83% off input tokens

Caching drops input cost from $0.95 to $0.16/MTok. For repeated system prompts, that saves $0.002371 per call.

Batch API: 40% off all tokens

Submit requests asynchronously and receive 40% off all token costs. This works best for offline pipelines where latency requirements are flexible.

Monthly cost projections

Monthly volumeCost per callStandard monthlyCached monthlyYou save with caching
10,000 calls$0.004850$48.5$24.79$23.71
50,000 calls$0.004850$242.5$123.94$118.56
100,000 calls$0.004850$485$247.88$237.12
500,000 calls$0.004850$2,425$1,239.4$1,185.6
1,000,000 calls$0.004850$4,850$2,478.8$2,371.2

Kimi K2 quality benchmarks for email summarization

Benchmark data for email summarization 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.

Alternative models for email summarization

10 models score 80+ on email summarization benchmarks, sorted by input rate. Cheapest: Gemini 3 Flash (38% less, 83/100 quality).

ModelProviderInput $/MTokOutput $/MTokQualityPer callvs Kimi
Gemini 3 FlashGemini$0.50$3.0083/100$0.00300038% cheaper
Gemini 3.6 FlashGemini$0.75$3.7575/100$0.00412515% cheaper
Gemini 3.7 FlashGemini$0.75$3.7579/100$0.00412515% cheaper
Gemini 3.8 FlashGemini$0.75$3.7581/100$0.00412515% cheaper
Claude Haiku 4.5Claude$1.00$5.0082/100$0.00550013% more
Gemini 3.5 FlashGemini$1.50$9.0076/100$0.00900086% more
Gemini 3.1 ProGemini$2.00$12.0080/100$0.0120147% more
Grok 4.7Grok$2.00$6.0075/100$0.00900086% more
Claude Fable 5Claude$10.00$50.0077/100$0.05501034% more
GPT 6 AstraGPT$10.00$50.0076/100$0.05501034% more

About Kimi K2

Provider

Moonshot (Kimi)

Tier

Mid-tier

Context

256K tokens

Released

Not published

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)

ModelTierInput $/MTokOutput $/MTokContext
Kimi K3Flagship$3.00$15.001M

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

How much does Kimi K2 cost for email summarization per API call?

A typical email summarization call using Kimi K2 costs about $0.004850 per call. This estimate uses 3,000 input tokens and 500 output tokens at standard rates. Standard rates are $0.95 per million input tokens and $4.00 per million output tokens.

Is Kimi K2 the best model for email summarization?

Our benchmark data does not yet include a email summarization ranking for Kimi K2. We recommend testing it against your own evaluation criteria before production usage.

What is the cheapest model for email summarization with good quality?

The most affordable model for email summarization above 80/100 quality is Gemini 3 Flash. It comes from Gemini at $0.50 per million input tokens. Output costs $3.00 per million tokens with a quality score of 83/100. Switching from Kimi K2 to Gemini 3 Flash saves about 38% per API call.

How can I reduce my Kimi K2 costs for email summarization?

Three strategies lower Kimi K2 costs for this task type effectively. Prompt caching gives a 83% discount on input tokens when reusing system prompts. The batch API gives 40% off all tokens for asynchronous processing workloads. Prompt optimization reduces token counts through more concise instructions and context windows. Combining caching with batching brings your cost from $0.004850 to about $0.000489 per call.

How does Kimi K2 pricing compare to other Moonshot (Kimi) models?

Moonshot (Kimi) has 1 other models alongside Kimi K2 today: Kimi K3 at $3.00/$15.00 per million tokens (flagship). The right choice depends on whether your email summarization workload prioritizes quality, cost, or speed.

What context window does Kimi K2 support?

Kimi K2 supports a context window of 256K tokens per API call. This sets the maximum combined length of your input prompt and generated response. For email summarization, typical calls use 3,000 input and 500 output tokens. That is well within this limit, leaving room for longer prompts or multi-turn conversations.

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This calculator estimates costs based on published rates only. Connect your API usage to see where your money goes and which models drive your spend.