One stable API surface
Call NSFW Image Detection with an APIAny key and a documented request format. Keep your application integration stable while model versions and upstream routes evolve behind the gateway.
Scene illustration, not an output of this model
Model:
Price: from 0.5 credits per request · ≈ $0.0003High stability with detailed usage records on the APIAny platform.
Prompt preset — no generated result yet
Publicly reachable image URL to classify.
USD estimate ≈ $0.0003: $999 tier: $1 = 2,000 credits.
History
Saved locally in this browser
0 running · 0 completed
Billing Rules
Authentication
Every request needs a Bearer token in the Authorization header. Create an API key in the console.
Authorization: Bearer YOUR_API_KEYhttps://apiany.ai/v1/moderationsRequest parameters
| Parameter | Type | Required | Notes |
|---|---|---|---|
model | string | Required | Model identifier to invoke. Use this model's ID. |
image_url | string | Optional | Publicly reachable image URL to classify. |
Request example
curl "https://apiany.ai/v1/moderations" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "nsfw-image-detection",
"image_url": ""
}'Response example
{
"id": "modr_abc123",
"object": "moderation",
"model": "nsfw-image-detection",
"results": [
{
"flagged": false,
"label": "safe",
"score": 0.02
}
]
}Model overview
NSFW Image Detection classifies uploaded or remote images for adult-content risk so applications can automate moderation, review queues, and publishing safeguards.
Measure throughput, latency, review effort, and unit economics against the operating target this model supports.
NSFW Image Detection is available on APIAny as one documented API surface with live pricing, model status, request examples, and an on-page playground. Teams can evaluate the model here, then keep authentication, usage records, and production routing in the same platform.
Test an image with NSFW Image Detection
Sources
The quotations below are from official API documentation that APIAny implements against.
The Chat Completions API endpoint will generate a model response from a list of messages comprising a conversation.
The Gemini API provides access to Google's most capable generative AI models.
APIAny advantages
NSFW Image Detection on APIAny is a production route with one key, live prices, status, and usage records, so you do not maintain a separate provider integration.
Call NSFW Image Detection with an APIAny key and a documented request format. Keep your application integration stable while model versions and upstream routes evolve behind the gateway.
Review the live NSFW Image Detection pricing rules before a request, estimate credits in the playground, and inspect request-level usage records after the call completes.
Use cases
The NSFW Image Detection API is an image safety check that runs before content reaches a public surface. Evaluate classification behavior with representative traffic and set thresholds from your own moderation policy.
01
Check images with NSFW Image Detection before they become public, then map the returned safety result to allow, block, or review actions under your own policy.
Test an image with NSFW Image Detection
02
Screen avatars, post images, comment attachments, and marketplace uploads with NSFW Image Detection before they enter user-facing surfaces.
Test an image with NSFW Image DetectionIntegration
Integrating NSFW Image Detection is creating an authenticated moderation request, submitting the image to inspect, and mapping the returned classification to your product policy. Deploy with bounded inputs, observable task states, and clear escalation or fallback paths.
Create an APIAny key and grant only the model access and budget required by the moderation service.
Use the endpoint and request shape shown on this page to send a supported image to NSFW Image Detection.
Interpret the returned safety result with your thresholds, then allow, block, or route the image to human review and record the decision.
Core capabilities
NSFW Image Detection features are image submission, machine-readable safety classification, policy routing, latency, and operating cost. Live request fields remain in the API Reference on this page.
/01
Submit a supported uploaded or remotely hosted image to NSFW Image Detection for content safety classification; this endpoint analyzes the image and does not generate or edit it.
/02
Return a machine-readable moderation result that your application can map to policy actions, review queues, or publishing rules.
/03
Map the machine-readable NSFW Image Detection result to product-specific allow, block, or human-review thresholds.
/04
Use the faster NSFW Image Detection route for interactive experiences, rapid iteration, and workloads where response time matters.
FAQ
Direct answers about capabilities, use cases, integration, and choosing the right NSFW Image Detection route.
NSFW Image Detection classifies uploaded or remote images for adult-content risk so applications can automate moderation, review queues, and publishing safeguards. APIAny makes this model callable through an authenticated API with live pricing, status information, request examples, and a browser playground on the same page.
Use NSFW Image Detection for pre-publication screening, user-generated image moderation, and human-review routing. It analyzes existing images; it does not create or edit images.
Use model status, channel health, retry policy, and usage logs to operate NSFW Image Detection as part of a production workflow instead of treating it as an isolated demo.
03
Route uncertain or high-risk NSFW Image Detection results to a review queue and retain the decision context needed for moderation audits.
Test an image with NSFW Image Detection
/05
Run higher-volume NSFW Image Detection workloads while using live pricing and request-level usage records to control spend.
Create an APIAny key, use the moderation endpoint documented on this page, and submit a supported image with the selected NSFW Image Detection identifier. Inspect the returned classification before applying a product policy action.
Test representative safe, unsafe, and borderline images, then set product-specific thresholds for allow, block, and human review. Monitor false positives, false negatives, latency, and review volume after launch.