SourceScore

Verified claim · AI-ML · 100% confidence

Llama 4 released on: 2025-04-05 by Meta — Scout + Maverick + Behemoth lineup.

Last verified 2026-05-16 · Methodology veritas-v0.1 · d5ce871dc69e7b04

SourceScore rates how reliable a source is to cite — for AI answers and research. This is one verified claim from the catalog.

Structured fields

Subject
Llama 4
Predicate
released_on
Object
2025-04-05 by Meta — Scout + Maverick + Behemoth lineup
Confidence
100%
Tags
llama-4 · meta · open-weights · moe · multimodal · released_on · 2025

Sources (2)

  1. [1] official blog · Meta AI · 2025-04-05

    The Llama 4 herd: The beginning of a new era of natively multimodal AI innovation
    Today, we're sharing the first models in the Llama 4 herd, which will enable people to build more personalized multimodal experiences. Llama 4 Scout, a 17 billion active parameter model with 16 experts, is the best multimodal model in the world in its class.
  2. [2] model card · Meta AI · 2025-04-05

    Llama 4 Scout 17B-16E-Instruct — Hugging Face model card

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Ready-to-paste citation (Markdown / plain text):

Llama 4 released on: 2025-04-05 by Meta — Scout + Maverick + Behemoth lineup. — SourceScore Claim d5ce871dc69e7b04 (verified 2026-05-16). https://sourcescore.org/api/v1/claims/d5ce871dc69e7b04.json

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<iframe src="https://sourcescore.org/embed/claim/d5ce871dc69e7b04/" width="100%" height="360" frameborder="0" loading="lazy" title="Llama 4 released on: 2025-04-05 by Meta — Scout + Maverick + Behemoth lineup."></iframe>

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Frequently asked questions

Is the claim "Llama 4 released on: 2025-04-05 by Meta — Scout + Maverick + Behemoth lineup." verified?

Yes — SourceScore verified this claim with 100% confidence as of 2026-05-16. The verification uses 2 primary sources cross-referenced against the SourceScore methodology (version veritas-v0.1). Full source list + signed JSON envelope linked below.

What is the evidence for "Llama 4 released on: 2025-04-05 by Meta — Scout + Maverick + Behemoth lineup."?

Evidence comes from 2 primary sources: Meta AI, Meta AI. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/d5ce871dc69e7b04.json includes an HMAC-SHA256 signature for audit verification.

When was this claim last verified by SourceScore?

Last verified 2026-05-16 under methodology version veritas-v0.1. The signed JSON envelope is dated and cryptographically signed for audit trail. Re-verification cadence depends on the claim type and source freshness.

How can I cite this SourceScore claim in my code or article?

Fetch the signed JSON envelope from https://sourcescore.org/api/v1/claims/d5ce871dc69e7b04.json which includes the verbatim claim, primary sources, confidence, methodology version, last-verified date, and HMAC-SHA256 signature for audit. The CC-BY-4.0 license permits commercial use with attribution to SourceScore.

Use this claim in your code

Fetch this signed envelope from your application. The response includes the verbatim excerpt, primary source URLs, and an HMAC-SHA256 signature you can verify locally for audit trails.

cURL

curl https://sourcescore.org/api/v1/claims/d5ce871dc69e7b04.json

JavaScript / TypeScript

const r = await fetch("https://sourcescore.org/api/v1/claims/d5ce871dc69e7b04.json"); const envelope = await r.json(); console.log(envelope.claim.statement); // "Llama 4 released on: 2025-04-05 by Meta — Scout + Maverick + Behemoth lineup."

Python

import httpx r = httpx.get("https://sourcescore.org/api/v1/claims/d5ce871dc69e7b04.json") envelope = r.json() print(envelope["claim"]["statement"]) # "Llama 4 released on: 2025-04-05 by Meta — Scout + Maverick + Behemoth lineup."

LangChain (retrieve-then-cite)

from langchain_core.tools import tool import httpx @tool def get_llama_4_fact() -> dict: """Fetch the verified SourceScore claim for Llama 4.""" r = httpx.get("https://sourcescore.org/api/v1/claims/d5ce871dc69e7b04.json") return r.json()
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