Kimi K2 API Pricing: Complete Cost Guide (2026)

Kimi K2 is Moonshot AI's flagship large language model, positioning itself as a strong competitor to GPT-4o and Claude 3.5 Sonnet at a fraction of the cost. With its massive context window and competitive benchmarks, Kimi K2 has attracted significant attention from developers looking for cost-effective alternatives to Western LLM providers.

This guide covers everything you need to know about Kimi K2 pricing, including per-token costs, context window pricing, batch discounts, and how it compares to competing models.

Kimi K2 Pricing Overview

Kimi K2 is available through Moonshot AI's API platform and through several third-party providers. Here is the current pricing structure:

Component	Price
Input tokens	$0.60 per 1M tokens
Output tokens	$2.00 per 1M tokens
Context window	Up to 128K tokens
Cached input tokens	$0.15 per 1M tokens
Maximum output	8,192 tokens per request

These prices position Kimi K2 as one of the most affordable frontier-class models available, significantly undercutting GPT-4o and Claude 3.5 Sonnet.

Pricing Through Third-Party Providers

Kimi K2 is also available through API aggregators and cloud platforms, sometimes at different price points:

Provider	Input (per 1M)	Output (per 1M)	Notes
Moonshot AI (direct)	$0.60	$2.00	Official pricing
OpenRouter	$0.60	$2.00	Pass-through pricing
Together AI	$0.60	$2.00	Available on demand
Amazon Bedrock	Varies	Varies	Check AWS pricing page
Fireworks AI	$0.60	$2.00	Optimized inference

Most third-party providers match Moonshot's official pricing, though some may add small margins for their infrastructure and support.

How to Access the Kimi K2 API

Direct Access via Moonshot AI

# Sign up at platform.moonshot.ai and get your API key

# Test with curl
curl https://api.moonshot.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $MOONSHOT_API_KEY" \
  -d '{
    "model": "kimi-k2",
    "messages": [
      {"role": "user", "content": "Explain quantum computing in simple terms"}
    ],
    "max_tokens": 1024
  }'

Using the OpenAI-Compatible SDK

Kimi K2's API is OpenAI-compatible, so you can use the standard OpenAI Python or JavaScript SDK:

from openai import OpenAI

client = OpenAI(
    api_key="your-moonshot-api-key",
    base_url="https://api.moonshot.ai/v1"
)

response = client.chat.completions.create(
    model="kimi-k2",
    messages=[
        {"role": "system", "content": "You are a helpful coding assistant."},
        {"role": "user", "content": "Write a Python function to merge two sorted arrays"}
    ],
    max_tokens=2048,
    temperature=0.7
)

print(response.choices[0].message.content)

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.MOONSHOT_API_KEY,
  baseURL: 'https://api.moonshot.ai/v1'
});

const response = await client.chat.completions.create({
  model: 'kimi-k2',
  messages: [
    { role: 'user', content: 'Write a React hook for infinite scrolling' }
  ],
  max_tokens: 2048
});

console.log(response.choices[0].message.content);

Via OpenRouter

from openai import OpenAI

client = OpenAI(
    api_key="your-openrouter-key",
    base_url="https://openrouter.ai/api/v1"
)

response = client.chat.completions.create(
    model="moonshot/kimi-k2",
    messages=[
        {"role": "user", "content": "Explain the difference between REST and GraphQL"}
    ]
)

Cost Comparison: Kimi K2 vs. Competitors

Here is how Kimi K2 stacks up against other frontier models on price:

Model	Input (per 1M)	Output (per 1M)	Context	Relative Cost
Kimi K2	$0.60	$2.00	128K	1x (baseline)
GPT-4o	$2.50	$10.00	128K	4-5x more
Claude 3.5 Sonnet	$3.00	$15.00	200K	5-7.5x more
Claude 3.5 Haiku	$0.80	$4.00	200K	1.3-2x more
Gemini 1.5 Pro	$1.25	$5.00	1M	2-2.5x more
DeepSeek V3	$0.27	$1.10	128K	0.5x less
Llama 3.1 405B (Fireworks)	$3.00	$3.00	128K	1.5-5x more

Kimi K2 is significantly cheaper than GPT-4o and Claude while achieving competitive benchmark scores, particularly in coding, math, and reasoning tasks.

Estimating Your Monthly Costs

To estimate your costs, you need to understand your token usage patterns. Here are common scenarios:

Scenario 1: Chatbot Application

Metric	Value
Average input per message	~500 tokens
Average output per message	~300 tokens
Messages per day	10,000
Monthly messages	300,000

Monthly cost calculation:

Input: 300,000 x 500 = 150M tokens x $0.60/1M = $90
Output: 300,000 x 300 = 90M tokens x $2.00/1M = $180
Total: $270/month

The same workload on GPT-4o would cost approximately $1,275/month.

Scenario 2: Code Generation Tool

Metric	Value
Average input (code context)	~2,000 tokens
Average output (generated code)	~800 tokens
Requests per day	5,000
Monthly requests	150,000

Monthly cost calculation:

Input: 150,000 x 2,000 = 300M tokens x $0.60/1M = $180
Output: 150,000 x 800 = 120M tokens x $2.00/1M = $240
Total: $420/month

Scenario 3: Document Analysis

Metric	Value
Average input (long documents)	~20,000 tokens
Average output (summary)	~500 tokens
Documents per day	200
Monthly documents	6,000

Monthly cost calculation:

Input: 6,000 x 20,000 = 120M tokens x $0.60/1M = $72
Output: 6,000 x 500 = 3M tokens x $2.00/1M = $6
Total: $78/month

Context Window Pricing

Kimi K2's 128K context window means you can send up to 128,000 tokens of input in a single request. The pricing per token stays the same regardless of how much of the context window you use. However, be aware that:

Longer contexts increase latency (time to first token)
You pay for every token in the context, including system prompts
Cached input tokens (repeated prefixes) are discounted to $0.15/1M

Optimizing Context Costs

# BAD: Sending full document every request
response = client.chat.completions.create(
    model="kimi-k2",
    messages=[
        {"role": "system", "content": very_long_system_prompt},  # 10K tokens every time
        {"role": "user", "content": user_question}
    ]
)

# BETTER: Use caching-friendly prefixes
# Keep the same system prompt prefix to benefit from cached token pricing
# Structure messages so common content comes first
response = client.chat.completions.create(
    model="kimi-k2",
    messages=[
        {"role": "system", "content": standard_prefix + task_specific_suffix},
        {"role": "user", "content": user_question}
    ]
)

Rate Limits

Kimi K2 has the following default rate limits:

Tier	Requests per Minute	Tokens per Minute	Tokens per Day
Free	3	32,000	1,000,000
Tier 1	60	300,000	10,000,000
Tier 2	300	1,000,000	50,000,000
Enterprise	Custom	Custom	Custom

You automatically move to higher tiers based on your cumulative spend. Contact Moonshot AI for enterprise-level rate limits.

Batch Processing Discounts

For high-volume, non-time-sensitive workloads, Moonshot offers batch processing at reduced rates:

Component	Standard	Batch (50% off)
Input tokens	$0.60/1M	$0.30/1M
Output tokens	$2.00/1M	$1.00/1M

Batch requests are processed within a 24-hour window and are ideal for:

Bulk document processing
Dataset annotation
Content generation at scale
Evaluation and testing pipelines

When to Choose Kimi K2

Use Case	Kimi K2	Better Alternative
Cost-sensitive applications	Best choice	--
Coding tasks	Strong choice	Claude 3.5 Sonnet (if budget allows)
Long document analysis	Good (128K context)	Gemini 1.5 Pro (1M context)
Multi-language support	Strong (especially CJK)	GPT-4o (broadest language support)
Maximum quality	Competitive	Claude 3.5 Sonnet or GPT-4o
Lowest possible cost	Good	DeepSeek V3 (cheaper)

Wrapping Up

Kimi K2 offers frontier-class performance at prices 4-5x lower than GPT-4o and Claude 3.5 Sonnet. For teams building AI applications where cost is a significant factor, Kimi K2 is worth serious evaluation. The OpenAI-compatible API makes switching straightforward, and the 128K context window handles most use cases.

If you are building AI applications that need media generation alongside language models -- for creating images, videos, or talking avatars -- try Hypereal AI free -- 35 credits, no credit card required. You can pair Kimi K2 for text generation with Hypereal's media APIs for a cost-effective full-stack AI solution.