OpenAI Moderation vs Groq
Agent-native side-by-side. Scores are curated estimates on a 0–10 scale. Lock-in: higher = worse. Prefer JTBD picks via /api/decide when choosing for a job.
| Dimension | OpenAI Moderation | Groq |
|---|---|---|
| Overall | 8.6 | 8.0 |
| Capability fit | 8.2 | 7.8 |
| Price / performance | 9.8 | 9.0 |
| Latency / reliability | 9.2 | 8.2 |
| Developer & agent DX | 9.4 | 8.8 |
| Privacy / data retention | 7.5 | 7.0 |
| Lock-in risk (higher = worse) | 5.5 | 5.0 |
| Maturity / track record | 9.5 | 7.8 |
| Pricing | As of 2026-09-14: Free moderation endpoint — omni-moderation-latest for text+images (images ≤20 MB). Also inline moderation on Responses/Chat Completions via moderation.model. Categories include hate, harassment, self-ha… | Per-token with free tier; LPU inference; narrow production model set (Llama/Qwen/GPT-OSS etc.); batch discounts. Llama Guard 4 12B (model id meta-llama/llama-guard-4-12b) is still documented at console.groq.com/docs/mode… |
| Tagline | Default first-pass content filter for agent I/O. Pair with Llama Guard (Groq) or NeMo Guardrails when you… | Use when latency is the product. Do not build your whole company on one Groq model SKU — they rotate. |
OpenAI Moderation
Default first-pass content filter for agent I/O. Pair with Llama Guard (Groq) or NeMo Guardrails when you need open/custom policies. Strengths: Free, fast, zero infra; Text + image multimodal classification; Inline with generation APIs.
Strengths
- Free, fast, zero infra
- Text + image multimodal classification
- Inline with generation APIs
Weaknesses
- Fixed OpenAI taxonomy — not your policy engine
- Cannot self-host or fine-tune categories
- Audio not classified
Groq
Use when latency is the product. Do not build your whole company on one Groq model SKU — they rotate. Strengths: Blazing tokens/sec latency; Simple API and free tier for prototypes; Great for real-time agent loops on supported models.
Strengths
- Blazing tokens/sec latency
- Simple API and free tier for prototypes
- Great for real-time agent loops on supported models
Weaknesses
- Limited model variety
- Aggressive model deprecations
- Not for frontier closed models
More pairs: anthropic-vs-openai · openai-vs-google-ai · groq-vs-fireworks · fireworks-vs-together · qdrant-vs-pinecone · pinecone-vs-supabase-pgvector · e2b-vs-modal · browserbase-vs-firecrawl