Capability · Comparison
Llama Guard 3 vs OpenAI Moderation
LLM safety classifiers scan inputs and outputs for harmful content before they reach users. Llama Guard 3 is Meta's open-weights safety model (8B and smaller variants) that you self-host. OpenAI Moderation is a free API endpoint on the OpenAI platform. Both cover standard harm categories (violence, self-harm, sexual content, hate, etc.) but differ on customization, latency, and deployment model.
Side-by-side
| Criterion | Llama Guard 3 | OpenAI Moderation |
|---|---|---|
| Access model | Open weights (self-host) | Free API endpoint |
| License | Llama Community License | OpenAI Terms — free to use |
| Parameter count | 8B (+ 1B small variant) | Undisclosed |
| Custom taxonomies | Yes — add or remove categories via prompt | No — fixed 13-category taxonomy |
| Multimodal | Vision variant (Llama Guard 3 11B Vision) | omni-moderation-latest has text + image |
| Multilingual coverage | Good (8 languages at release) | Very good (40+ languages) |
| Latency | Depends on self-host infra (typically 100-300ms on A100) | Fast — ~100-200ms round-trip |
| Cost | Self-host compute | Free |
| Data residency | Full control (VPC deployment) | OpenAI-hosted |
Verdict
For most teams starting a project, OpenAI Moderation is the right default: it's free, fast, and you don't have to deploy anything. It's good enough for consumer apps with standard harm categories. Llama Guard 3 becomes the right choice when you need custom safety categories (industry-specific, product-specific), when data residency requires self-hosting, or when your app is not OpenAI-resident and adding another vendor doesn't make sense. For regulated industries, Llama Guard 3 in-VPC is the cleaner story. For most others, don't over-engineer — OpenAI Moderation is fine.
When to choose each
Choose Llama Guard 3 if…
- You need custom safety categories beyond OpenAI's 13.
- Data residency / VPC deployment is required.
- You're already running Llama models and want a single ops story.
- You want to own the classifier weights and deploy on-prem.
Choose OpenAI Moderation if…
- You want a free, zero-ops safety layer.
- OpenAI's standard taxonomy matches your needs.
- You're already on the OpenAI / Azure OpenAI stack.
- You're prototyping and don't want to stand up infra.
Frequently asked questions
Do I really need a dedicated safety classifier if my LLM has built-in safety?
Yes in most production apps. The LLM's own refusals are probabilistic; a separate classifier lets you enforce policy deterministically and log violations. Defense in depth matters when the stakes are user-visible.
Can I run Llama Guard 3 on a single GPU?
Yes. The 8B model fits on a single 24GB GPU in bf16, or on ~8GB in 4-bit quantization. The 1B variant runs on CPU or tiny GPUs. For high throughput, batch many classifications per forward pass.
Is there an Anthropic equivalent?
Anthropic has a 'Constitutional' classifiers approach in beta and a Messages API option to add safety-focused system prompts. For dedicated classifier models, most Anthropic customers combine Claude's built-in refusals with an external classifier (Llama Guard, OpenAI Moderation, or Azure Content Safety).
Sources
- Meta — Llama Guard 3 — accessed 2026-04-20
- OpenAI — Moderation guide — accessed 2026-04-20