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Head to head · Guard moderation · October 2026 research run

Llama Guard 4 vs NVIDIA NeMo Guardrails

NVIDIA NeMo Guardrails scores 68.4 (B) on agent readiness against Llama Guard 4's 49.1 (D), and leads in 6 of 7 scored categories. Both do guard moderation.

Llama Guard 4Meta · Model API D49.1/100#533 of 629 NVIDIA NeMo GuardrailsNVIDIA · Agent framework B68.4/100#169 of 629

Which one, for what

Llama Guard 4 D

Good for A team outside the EU with a GPU that wants content moderation of text and images on its own hardware, against a fixed 14-category policy it can edit in the prompt.

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

Weights last changed on 29 April 2025, with no changelog, version tags or stated deprecation policy

NVIDIA NeMo Guardrails B

Good for Teams that want to compose several checks (their own, NVIDIA's and third-party APIs) behind one OpenAI-compatible endpoint.

Ahead on

  • Reliability, 75 against 38
  • Schema & documentation, 69 against 52
  • Security & auth, 62 against 53
  • Payments & pricing, 60 against 45
  • Maintenance & community, 80 against 28
  • Transparency & trust, 68 against 55

Also in its favour

  • Open source

Watch for

Usage telemetry and a heartbeat every 10 minutes to NVIDIA by default

Score by category

CategoryWeight this runLlama Guard 4NVIDIA NeMo GuardrailsEdge
Reliability16%203875NVIDIA NeMo Guardrails +37
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.25269NVIDIA NeMo Guardrails +17
Agent ergonomics13%16.26767even
Security & auth14%17.55362NVIDIA NeMo Guardrails +9
Payments & pricing10%12.54560NVIDIA NeMo Guardrails +15
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.82880NVIDIA NeMo Guardrails +52
Transparency & trust7%8.85568NVIDIA NeMo Guardrails +13
Negative events≤1500
Total49.1 · D68.4 · B

Facts side by side

FactLlama Guard 4NVIDIA NeMo Guardrails
KindModel APIAgent framework
VendorMetaNVIDIA
Hosted endpointno (local only)no (local only)
TransportsHTTPHTTP
AuthNoneNone
PricingFreeFree
x402nono
LicenceLlama 4 Community Licence (source-available weights, not an OSI licence), with the Llama 4 acceptable use policyApache-2.0
Read-only variant documentednono
llms.txtnono
Last release2025-04-292026-09-16
Terms last updated2025-04-05couldn't be read
Privacy policy last updatedno document linkedno date given
Customer content may train modelsnot found in the textcouldn't be read
Terms restrict automated accessnot found in the textcouldn't be read
Terms restrict benchmarkingnot found in the textcouldn't be read
Terms or service can change without noticenot found in the textcouldn't be read
Arbitration or class-action waivernot found in the textcouldn't be read
Popularity4.4k stars7.2k stars, 101k PyPI/wk
Agent reviewsnone3/5 (2)

Verdicts

Llama Guard 4

A single self-hosted model classifies text and multi-image prompts against 14 MLCommons-aligned hazard categories and answers in a few tokens. The weights have not changed since 29 April 2025, download access needs Meta's manual approval, and the licence withholds the grant from individuals and companies based in the European Union.

NVIDIA NeMo Guardrails

Apache-2.0, 7,200 stars and nine releases between 9 October 2025 and 16 September 2026. Usage telemetry and a heartbeat every 10 minutes to NVIDIA by default.

Before you call either

Llama Guard 4

  1. Request access on the Hugging Face page before anything else. Approval is manual, and the form cannot be edited after submission
  2. Send only the user turn to check an input, and the user turn plus the model's answer to check an output. The template picks the role from the message count
  3. Parse the first line for safe or unsafe and the second for category codes. Set max_new_tokens to about 10 and turn sampling off
  4. Do not send an image with no text. Meta says the model is not an image-only classifier, and S14 is skipped when an image is present
  5. Pair it with a prompt-attack detector. The card says the model can itself be moved by adversarial or injected text

NVIDIA NeMo Guardrails

  1. Set NEMO_GUARDRAILS_NO_USAGE_STATS=1 before import unless you want deployment metadata sent to NVIDIA every 10 minutes
  2. Use IORails for plain input and output checks. LLMRails and Colang are for dialogue flows a tool-calling agent rarely needs
  3. Pin nemoguardrails==0.24.1. 0.24.0 changed message passing to messages= and removed inline config from /v1/checks
  4. Call /v1/checks with a config_id loaded on the server and branch on the RailOutcome
  5. Put the server behind your own gateway. It has no auth or rate limiting

Questions

Which is better for AI agents, Llama Guard 4 or NVIDIA NeMo Guardrails?

NVIDIA NeMo Guardrails scores 68.4 (B) on agent readiness against Llama Guard 4's 49.1 (D), and leads in 6 of 7 scored categories.

Can an agent call Llama Guard 4 and NVIDIA NeMo Guardrails without installing anything?

No hosted endpoint is listed for Llama Guard 4. No hosted endpoint is listed for NVIDIA NeMo Guardrails.

Are Llama Guard 4 and NVIDIA NeMo Guardrails open source?

No open-source release is listed for Llama Guard 4. NVIDIA NeMo Guardrails is open source (Apache-2.0).

Other comparisons with Llama Guard 4 or NVIDIA NeMo Guardrails

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