Access Moonshot AI's Kimi model. Famous for its 200K+ context window — process entire books, codebases, and document collections in one call.
Get API Key — $1 Free Credit View All ModelsAccess Moonshot AI's Kimi model. Famous for its 200K+ context window — process entire books, codebases, and document collections in one call. Moonshot AI (月之暗面) has built one of the most capable models available, and through TokenEase you can access it from anywhere in the world.
Process entire books, research papers, and codebases. No other model matches this context length at this price.
Upload and analyze PDFs, research papers, legal documents — Kimi reads and understands them all.
Perfect for academic research — summarize papers, extract key findings, compare sources.
Analyze entire GitHub repositories. Understand architecture, find bugs, generate documentation.
| Metric | TokenEase Price |
|---|---|
| Input tokens | $2.00 / million |
| Output tokens | $8.00 / million |
| Context window | 200,000+ tokens |
| Free trial | $1 credit (~500000 tokens) |
from openai import OpenAI
client = OpenAI(
base_url="https://tokenease.io/v1",
api_key="sk-your-tokenease-key"
)
response = client.chat.completions.create(
model="kimi",
messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)
Process dozens of research papers at once. Extract findings, compare methodologies, generate literature reviews.
Analyze entire case files, contracts, and regulatory documents. Find relevant clauses and potential issues.
Review entire codebases for architecture issues, security vulnerabilities, and performance problems.
Process annual reports, earnings calls, and market data for investment analysis.
Kimi has the largest context window of any commercially available Chinese AI model — 200K+ tokens. This means you can process entire books or codebases in a single API call.
GPT-4o has 128K context. Kimi's 200K+ is 56% larger, allowing you to process significantly more content at once.
Kimi is pricier per token due to its large context capability, but for document-heavy use cases it can actually save money by reducing the number of API calls needed.