Hive Moderation AI Detector Review

Pricing

API-only. Enterprise pricing by volume. Contact sales.

Compare all AI detector pricing plans on our pricing comparison page.

Hive Moderation AI Detector: Full Review

Hive Moderation is the only tool in our benchmark that detects AI-generated content across text, images, video, and audio. Founded in 2017 by Kevin Guo and Dmitriy Karpman, Hive built its reputation on content moderation APIs before expanding into synthetic media detection. For media companies, social platforms, and trust-and-safety teams dealing with deepfakes across multiple formats, Hive is the clear market leader.

In this review I focus on Hive's text detection capabilities alongside its multimodal strengths. If you need text-only detection, Originality.ai is more accurate. But if you need to detect AI-generated images, voice clones, or deepfake video alongside text, no other tool comes close to Hive.

How Hive Moderation Detects AI Content

Hive runs specialized detection models for each media type. The text detection model analyzes linguistic patterns and statistical distributions similar to other detectors. What makes Hive different is the breadth of its detection stack:

Text detection. Identifies AI-generated text from major language models including GPT-4o, Claude, Gemini, and Llama. Returns a confidence score and classification.

Image detection. Classifies images as real or AI-generated, identifying output from Midjourney, DALL-E 3, Stable Diffusion, and other popular generators. This is increasingly critical as AI-generated images become harder to distinguish visually.

Audio detection. Identifies voice clones and AI-generated speech from text-to-speech systems like ElevenLabs, as well as dedicated voice cloning frameworks. This is Hive's strongest differentiator.

Video detection. Analyzes video content for signs of deepfake manipulation, including face swaps and AI-generated footage. Still an emerging capability but Hive is among the most advanced implementations available.

Hive Accuracy: Our Benchmark Results

In our March 2026 benchmark across 2,400 text samples, Hive achieved 88% overall accuracy with a 9% false positive rate and a 12% false negative rate. This places Hive second in accuracy behind Originality.ai (91%).

The text accuracy at 88% is strong and places Hive in the top tier of detectors. But the real story is the multimodal performance. Our audio benchmark tested 600 clips spanning 8 text-to-speech systems and 4 voice cloning frameworks. Hive achieved 88% accuracy with a 9% FPR on voice clone detection — a capability no other tool in our text-focused benchmark offers.

Voice Deepfake Detection: Hive's Standout Capability

Voice deepfakes are one of the fastest-growing threats in synthetic media. Services like ElevenLabs can clone a voice from a few seconds of audio, and the results are increasingly convincing to human listeners. Hive's voice detection model analyzes acoustic patterns, spectral features, and temporal characteristics to identify synthetic speech.

In our testing, Hive performed particularly well against high-quality voice clones from ElevenLabs and Resemble AI. It struggled more with lower-quality TTS systems where the synthetic artifacts are paradoxically easier for humans to hear but harder for automated systems to classify consistently.

For organizations dealing with voice-based fraud, unauthorized AI-generated podcasts, or deepfake audio in news media, Hive is currently the best available tool. This is a market where few alternatives exist at all, giving Hive a significant first-mover advantage.

Hive for Image Detection

Hive's image detection capability classifies images as real or AI-generated with 91% accuracy in our testing. The model identifies output from major generators including Midjourney, DALL-E 3, Stable Diffusion XL, and Flux. It returns both a classification and a confidence score.

This is becoming increasingly important as AI-generated images proliferate on social media, news sites, and e-commerce platforms. Stock photo agencies, news organizations, and social media trust-and-safety teams are the primary users of this capability.

API-First Architecture

Hive operates as an API-only service. There is no web dashboard where you paste text and get a result. You integrate Hive into your application through its API, which returns JSON responses with detection classifications and confidence scores for each media type.

The 340ms text detection latency is competitive — faster than Originality.ai (420ms) and Copyleaks (510ms), though slower than Writer.com's industry-leading 290ms. For teams building content moderation pipelines, the combined multimodal capability at reasonable latency is the key selling point.

Hive Pricing

Hive uses enterprise volume-based pricing. There is no self-serve plan, no free tier, and no published price list. You contact sales, describe your volume and use case, and negotiate a contract. This model is standard for enterprise API products but creates a barrier for smaller teams and individual developers.

Based on publicly available information and conversations with Hive customers, pricing typically scales with the number of API calls per month and the media types you need. Text detection alone is less expensive than the full multimodal suite. Enterprise contracts include SLAs, dedicated support, and data processing agreements for compliance-sensitive organizations.

Where Hive Falls Short

No consumer product. Without a web interface, Hive is inaccessible to anyone without developer resources. A teacher who wants to check a student paper cannot use Hive. A freelance editor who wants to scan an article cannot use Hive. This is an API product for engineering teams building detection into their own applications.

Enterprise pricing only. The lack of self-serve pricing eliminates Hive from consideration for individuals, small agencies, and most educational institutions. If you need a tool you can start using today, look at GPTZero or Originality.ai.

Text detection is not its core strength. While 88% text accuracy is strong, it trails Originality.ai at 91%. If text detection is your only need, you are paying for multimodal capability you will not use.

Hive vs Originality.ai: When to Choose Which

Hive Moderation FAQ

The Bottom Line on Hive Moderation

Hive Moderation is the best multimodal AI detection tool available in 2026. If your organization needs to detect AI-generated text, images, audio, and video through a single API, Hive is the only serious option. The 88% text accuracy is strong, and the voice and image detection capabilities are market-leading.

The trade-off is accessibility. API-only access and enterprise pricing mean Hive is exclusively for organizations with developer resources and meaningful budgets. For text-only detection with self-serve access, Originality.ai is more accurate and more affordable. For a complete picture, see our AI detector comparison and accuracy benchmarks.

Pros & Cons

Technical Details

For developers building detection into their own workflows, see our API documentation for integration guides and code samples.

Methodology

Accuracy from testing on 2,400 samples: 1,200 human-written and 1,200 AI-generated from Claude 3.5, GPT-4o, Gemini 1.5 Pro, and Llama 3.1. See full methodology. Learn more about how AI detection works.