EmpirioLabs AI

Overview

Property	Details
Description	EmpirioLabs AI hosts open, proprietary, and custom models behind one OpenAI-compatible API with pay-as-you-go pricing across text, image, video, audio, search, and 3D endpoints.
Provider Route on LiteLLM	empiriolabs/
Link to Provider Doc	EmpirioLabs AI Documentation ↗
Base URL	https://api.empiriolabs.ai/v1
Supported Operations	/chat/completions, /responses

We support ALL EmpirioLabs chat models, just set empiriolabs/ as a prefix when sending completion requests

Available Models (selection)

The full live catalog with pricing is at empiriolabs.ai/models. Popular chat models:

Model	Description	Context Window
empiriolabs/qwen3-7-max	Qwen3.7 Max flagship text model for coding, agents, and deep thinking	1M tokens
empiriolabs/qwen3-7-plus	Cost-effective Qwen3.7 vision-language model (text, image, video input)	1M tokens
empiriolabs/deepseek-v4-pro	DeepSeek V4 flagship MoE (1.6T total / 49B active parameters)	1M tokens
empiriolabs/deepseek-v4-flash	Lightweight DeepSeek V4 MoE (284B total / 13B active parameters)	1M tokens
empiriolabs/glm-5-1	Zhipu AI long-context reasoning model with tool use	202K tokens
empiriolabs/kimi-k2-6	Moonshot Kimi K2.6 multimodal reasoning model	256K tokens
empiriolabs/minimax-m3	MiniMax M3 multimodal reasoning for coding and agents	524K tokens
empiriolabs/gemma-4-26b-a4b	Google Gemma 4 26B A4B open multimodal model	256K tokens

Required Variables

Environment Variables

os.environ["EMPIRIOLABS_API_KEY"] = ""  # your EmpirioLabs API key

Get an API key from the EmpirioLabs dashboard.

Usage - LiteLLM Python SDK

Non-streaming

EmpirioLabs Non-streaming Completion

import os
import litellm
from litellm import completion

os.environ["EMPIRIOLABS_API_KEY"] = ""  # your EmpirioLabs API key

messages = [{"content": "Hello, how are you?", "role": "user"}]

# EmpirioLabs call
response = completion(model="empiriolabs/qwen3-7-plus", messages=messages)

print(response)

Streaming

EmpirioLabs Streaming Completion

import os
import litellm
from litellm import completion

os.environ["EMPIRIOLABS_API_KEY"] = ""  # your EmpirioLabs API key

messages = [{"content": "Hello, how are you?", "role": "user"}]

# EmpirioLabs call with streaming
response = completion(
    model="empiriolabs/qwen3-7-plus",
    messages=messages,
    stream=True,
)

for chunk in response:
    print(chunk)

Usage - LiteLLM Proxy

Add the following to your LiteLLM Proxy configuration file:

config.yaml

model_list:
  - model_name: qwen3-7-plus
    litellm_params:
      model: empiriolabs/qwen3-7-plus
      api_key: os.environ/EMPIRIOLABS_API_KEY

  - model_name: deepseek-v4-flash
    litellm_params:
      model: empiriolabs/deepseek-v4-flash
      api_key: os.environ/EMPIRIOLABS_API_KEY

Start your LiteLLM Proxy server:

Start LiteLLM Proxy

litellm --config config.yaml

# RUNNING on http://0.0.0.0:4000

OpenAI SDK

EmpirioLabs via Proxy

from openai import OpenAI

# Initialize client with your proxy URL
client = OpenAI(
    base_url="http://localhost:4000",  # Your proxy URL
    api_key="your-proxy-api-key",      # Your proxy API key
)

# Non-streaming response
response = client.chat.completions.create(
    model="qwen3-7-plus",
    messages=[{"role": "user", "content": "hello from litellm"}],
)

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

cURL

EmpirioLabs via Proxy - cURL

curl http://localhost:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-proxy-api-key" \
  -d '{
    "model": "qwen3-7-plus",
    "messages": [{"role": "user", "content": "hello from litellm"}]
  }'

Additional Notes

• Thinking-capable models accept reasoning_effort (none, low, medium, high, max); the gateway maps it onto each model's native thinking controls.
• Per-model parameters, limits, and live pricing are listed at docs.empiriolabs.ai and on each model page at empiriolabs.ai/models.