- Developers & Platform Owners: Implement scalable, multilingual guardrails across apps, chatbots, forums, or user-generated content pipelines.
- Chat & Conversational AI Builders: Use the conversational endpoint for context-aware filtering of back-and-forth dialogues.
- Global Enterprises: Enforce compliance in 11 supported languages (e.g., Arabic, Chinese, English, French, German, Japanese, Korean, Portuguese, Russian, Spanish, Italian).
- Content Moderation Teams: Leverage raw scores to customize thresholds and fine-tune precision-recall balance for each category.
- Regulated Industries: Detect model-generated advice and PII to ensure safety and compliance in sensitive sectors.
How to Use Mistral Moderation API?
- Choose Endpoint: Use `classifiers.moderate` for raw text or `classifiers.moderate_chat` for conversational turns.
- Send Payload: Provide inputs as strings (raw) or conversation arrays (conversational endpoint) to Mistral’s Python, TypeScript, or cURL clients.
- Receive Category Scores: The API returns boolean flags and confidence scores for each category (e.g., `‘violenceandthreats’: true, score 0.999`).
- Adjust Policy Thresholds: Use raw scores to tailor sensitivity per use case, or rely on default thresholds (precision 0.8–0.9, recall 0.7–0.99).
- Scale Safely: Use batch endpoint to reduce latency and cost (~25% savings for high-volume requests).
- Multilingual by Design: Supports 11 major languages beyond English, a key advantage over many moderation APIs.
- Dual-Endpoint Support: Handles both raw text and conversational filtering with context awareness.
- Fine-Grained Risk Scoring: Provides category-level confidence scores empowering nuanced policy control.
- Industry-Grade Guardrails: Designed to filter model-generated advice, PII, and harmful claims to improve downstream safety.
- Batching for Efficiency: Built-in batch API reduces cost and improves scalability for high-volume moderation needs.
- Strong multilingual support in 11 languages
- Both raw-text and conversational moderation endpoints
- Category-level scoring enables customizable policy tuning
- Backed by Ministral 8B with robust safety precision/recall
- Batch endpoint reduces costs and improves throughput
- Being early-stage, benchmark comparisons to alternatives like OpenAI or Perspective are limited
- Potential bias and false positives (e.g., in AAVE or disability-related terms) may still exist
- Not yet as customizable as full on-prem policy engines
API only
$0.1 per 1M input tokens
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Editorial Note
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