Multimodal content understanding
The Hive Vision Language Model processes images or image-and-text pairs and can return descriptions, answers, labels, or structured JSON from a single request.
Hive
Freemium
Hive provides APIs for understanding, searching, moderating, and generating text, image, video, and audio content. Developers can use its pre-trained and open-source models for content workflows such as moderation, classification, brand protection, and media generation.
Hiveとは？
Hive is an AI model platform for developers building applications that need to understand, search, moderate, or generate digital content. Its APIs work across text, images, video, and audio, covering tasks such as classification, moderation, media search, translation, speech-to-text, and generative media.
The platform includes dedicated models and the Hive Vision Language Model (VLM). The VLM accepts images or image-and-text pairs and returns plain-language answers or structured JSON in one call. Natural-language prompts can define labels and policies, while bias tuning allows teams to adjust classification behavior toward different precision and recall goals.
Hive also provides a Playground for testing models and usage-based plans for developer and enterprise needs. The enterprise offering includes access to all Hive models, a moderation dashboard, higher rate limits, premium support, and multi-region support for high-volume deployments.
Hiveでできること
Prompt-defined classification and moderation
Teams can describe concepts and policies in natural language instead of relying only on fixed label sets. Prompts can be revised as guidelines change, without retraining the model.
Context-aware visual analysis
The VLM is designed to connect visual content with accompanying text and identify nuanced cases such as harmful text in images, minors with alcohol, or context-dependent profanity.
Broad model catalog
Hive lists models for visual, text, audio, OCR, and video moderation; AI-generated media detection; object and scene analysis; people and identity-related detection; search; translation; and content generation.
Bias tuning controls
Prompt-level class weighting lets teams increase or decrease the emphasis on selected categories to support different false-positive and recall priorities.
API and Playground access
Developers can test Hive and open-source models in the Playground and deploy them through authenticated API requests, including multimodal chat-completions requests.
利用シーン
“Online community moderation”
Social and community platforms can review user-submitted images, text, audio, video, and OCR content for harmful material, then use model results to support filtering, escalation, or human review.
“Marketplace and platform safety”
Marketplaces and other digital platforms can combine content understanding with object, scene, logo, face, and AI-generated-media detection to assess listings or uploaded media.
“Brand protection and media search”
Brands, publishers, agencies, and rights-focused teams can search media collections and identify logos, people, locations, or related visual attributes across customer-provided or other datasets.
“Generative AI application workflows”
Teams building generative applications can use Hive’s available text-to-image models alongside detection and moderation capabilities to create or review generated media.
“Evolving policy and taxonomy review”
Organizations whose content rules change frequently can use the VLM’s natural-language prompts and bias tuning to test new labels or policy priorities without creating a separate classifier for every concept.
よくある質問
What types of content can Hive process?
Hive’s models address text, images, video, and audio. Listed capabilities include moderation, OCR, object and scene analysis, AI-generated-content detection, search, translation, speech-to-text, and media generation.
What is the Hive Vision Language Model used for?
The VLM accepts an image or an image-and-text pair and can produce plain-language answers or structured JSON. It is intended for flexible tagging, moderation, and detection tasks defined through prompts.
Should I use the VLM or a pre-trained classifier?
Hive positions the VLM for broad label coverage, changing policies, and niche or evolving content. Dedicated pre-trained classifiers are the better fit when the task has fixed classes and peak precision or recall is the main priority.
How can developers test or access Hive models?
Models can be tested in the Hive Playground and accessed through authenticated API requests. The home page shows a chat-completions request using an API key and multimodal text and image inputs.
How is Hive priced?
Hive uses usage-based pricing. The pricing page describes a developer offering with selected model access and an enterprise offering with all-model access and additional operational support; some higher limits and capabilities use a contact-sales flow.
クイック情報
- Category
- AI platform and developer APIs
- Primary inputs
- Text, images, video, and audio
- Core workflows
- Understand, search, moderate, detect, translate, and generate content
- Flexible model
- Hive Vision Language Model for prompt-defined multimodal analysis
- Access
- Playground testing and authenticated API requests
- Pricing model
- Usage-based developer and enterprise plans
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