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Free AI models via OpenCode API — no rate limits from Cloudflare

Base URL
https://janitor-pi.vercel.app
Endpoint
/v1/chat/completions

Setup guide →

Showcase

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Supported Models

MiMo v2.5 Thinking
Compact reasoning model optimized for math, code, and logical problem-solving. JanitorAI can show or hide thinking when available.
mimo-v2.5-free
DeepSeek V4 Flash Thinking
Fast inference model with strong coding and analytical performance. Thinking metadata is preserved for compatible clients.
deepseek-v4-flash-free
Nemotron 3 Ultra Thinking
High-performance model built for structured data extraction and complex reasoning with preserved thinking fields.
nemotron-3-ultra-free
North Mini Code North
Lightweight code-specialized model for quick completions, refactoring, and code explanation tasks.
north-mini-code-free
MythoMax Chub
Roleplay-optimized model with strong character consistency and creative storytelling capabilities.
mythomax
GLM 4.7 Cloudflare
Zhipu AI's latest multimodal model with strong reasoning, coding, and instruction-following. Supports thinking.
glm-4.7
GLM 5.2 Cloudflare
Zhipu AI's latest flagship with enhanced reasoning, coding, and instruction-following. Supports thinking.
glm-5.2
DeepSeek R1 Cloudflare
DeepSeek's distilled reasoning model (32B Qwen) with chain-of-thought for math, code, and logic.
deepseek-r1
Kimi K2.5 Cloudflare
Moonshot's latest model with strong reasoning and instruction following. Supports thinking.
kimi-k2.5
Qwen 2.5 Coder Cloudflare
Alibaba's 32B code-specialized model for coding, refactoring, and technical tasks.
qwen2.5-coder

Quick Start

# cURL
curl https://janitor-pi.vercel.app/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mimo-v2.5-free",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

# Python (openai SDK)
from openai import OpenAI

client = OpenAI(
    base_url="https://janitor-pi.vercel.app",
    api_key="anything"
)

response = client.chat.completions.create(
    model="deepseek-v4-flash-free",
    messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)