Kimi K3 API — Access Moonshot's #1 Model via OpenAI-Compatible Endpoint

K3 at a Glance

89.2%

MMLU-Pro (#1)

2.8T

Total Params (MoE)

256K

Context Window

Why Use K3 Through TokenEase?

Accessing Kimi K3 directly requires a Chinese phone number, Alipay, and KYC verification. Most developers outside China cannot sign up.

TokenEase removes every barrier:

No Chinese phone number — email-only registration
No Alipay — pay with credit card via Paddle
No KYC — start in 30 seconds
OpenAI-compatible — change base_url and model, done
1M free tokens — test before you pay

Quick Start

1. Get your API key

2. Replace two lines in your code

from openai import OpenAI

client = OpenAI(
    base_url="https://tokenease.io/v1",  # Change this
    api_key="your-tokenease-api-key"      # Change this
)

response = client.chat.completions.create(
    model="kimi-k3",  # Or kimi-k3-cached for cheaper cache hits
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain quantum computing in one paragraph."}
    ],
    max_tokens=500
)
print(response.choices[0].message.content)

3. That's it

Your existing OpenAI-based code works without changes. LangChain, LlamaIndex, Cursor, Claude Code — all compatible.

Pricing

K3 Input (cache miss) $3.50 / 1M tokens

K3 Input (cache hit) $0.50 / 1M tokens

K3 Output $18.00 / 1M tokens

K3 with TokenEase free trial $0 — 1M tokens included

Real-World Cost Comparison

Scenario	GPT-4o via OpenAI	K3 via TokenEase	Savings
50K requests/day, 2K in + 800 out	~$650/day	~$130/day	80%
Code review bot, 10K requests/day	~$2,500/day	~$500/day	80%
Customer support, mixed workload	$1,800/mo	$100/mo	94%

Model Variants

Model ID	Best for	Input	Output
`kimi-k3`	General use, highest quality	$3.50/M	$18.00/M
`kimi-k3-cached`	Repeated prompts (system prompts, templates)	$0.50/M	$18.00/M
`kimi-k2.7-code`	Code generation, debugging	$1.20/M	$5.00/M
`kimi-k2.6`	Balanced speed/quality	$1.20/M	$5.00/M
`kimi-k2.5`	Fast, cost-efficient	$0.80/M	$4.00/M

Frequently Asked Questions

Is K3 really better than GPT-4o?

On MMLU-Pro (a rigorous academic benchmark), K3 scores 89.2% vs GPT-4o's ~87.5%. In production A/B tests on code error explanations, K3 achieved 96% accuracy vs GPT-4o's 94%. For Chinese-language tasks, K3 is significantly stronger.

Do I need to change my existing code?

Only two lines: base_url and api_key. The model name changes to kimi-k3. Everything else — streaming, function calling, JSON mode — works identically.

How does the 256K context window help?

You can fit entire codebases, long documents, or multi-turn conversations without chunking. One developer reported passing full stack traces + related source modules in a single prompt, improving fix quality.

What about latency?

For inputs under 16K tokens, latency is comparable to GPT-4o. For 200K+ token inputs, first-token latency can reach 8–12 seconds. For real-time chatbots, keep inputs under 16K or use kimi-k2.5 for faster responses.

Start Free Trial — 1M Tokens Read API Docs