FACE DETECTION API
Detect faces with a single API call.
Use vision-language models to detect faces, analyze facial attributes, estimate age, identify emotions, and extract facial landmarks from images. Send an image with a prompt describing what you need, get structured results back. No specialized face detection library required.
QUICK START
Integrate in minutes.
from openai import OpenAI
client = OpenAI(
base_url="https://api.runcrate.ai/v1",
api_key="rc_live_YOUR_API_KEY",
)
response = client.chat.completions.create(
model="Qwen/Qwen3-VL-235B-A22B-Instruct",
messages=[
{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": "https://example.com/photo.jpg"}},
{"type": "text", "text": "Detect all faces in this image. For each face, provide bounding box coordinates, estimated age, and expression."},
],
}
],
)
print(response.choices[0].message.content)AVAILABLE MODELS
Models you can use today.
| Model | Provider | Price | Detail |
|---|---|---|---|
| Qwen/Qwen3-VL-235B-A22B-Instruct | Alibaba | Per-token | 235B MoE, top-tier visual understanding |
| meta-llama/Llama-3.2-90B-Vision-Instruct | Meta | Per-token | 90B, strong image reasoning |
| Qwen/Qwen3-VL-30B-A3B-Instruct | Alibaba | Per-token | 30B MoE, efficient processing |
WHY RUNCRATE
Built for production.
Vision-Language Detection
Use the full power of vision-language models for face detection. Ask for bounding boxes, age estimates, expressions, or any custom attribute in natural language.
Structured Output
Request JSON-formatted results with bounding boxes, confidence scores, and attributes. Parse programmatically for integration into your pipeline.
No Specialized Library
Skip OpenCV, dlib, and MediaPipe setup. Use the same chat completions API you already use for text, just add an image.
Custom Analysis
Beyond detection: ask the model to analyze facial expressions, estimate demographics, count people, or describe the scene context around each face.
FAQ