SourceScore

Verified claim · AI-ML · 100% confidence

Google Gemma 3 publicly released on: 2025-03-12 by Google DeepMind — Gemma 3 family (1B/4B/12B/27B), 128k context, multimodal vision.

Last verified 2026-05-16 · Methodology veritas-v0.1 · a832aaba30ee658e

Structured fields

Subject
Google Gemma 3
Predicate
publicly_released_on
Object
2025-03-12 by Google DeepMind — Gemma 3 family (1B/4B/12B/27B), 128k context, multimodal vision
Confidence
100%
Tags
gemma-3 · google · deepmind · open-weight · multimodal · released_on · 2025

Sources (2)

  1. [1] official blog · Google · 2025-03-12

    Introducing Gemma 3: The most capable model you can run on a single GPU or TPU
    Gemma 3 is our most capable, portable and responsibly developed open model family yet. Available in 1B, 4B, 12B and 27B parameter sizes with vision capabilities at 4B+.
  2. [2] model card · Google / Hugging Face · 2025-03-12

    gemma-3-27b-it on Hugging Face

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Google Gemma 3 publicly released on: 2025-03-12 by Google DeepMind — Gemma 3 family (1B/4B/12B/27B), 128k context, multimodal vision. — SourceScore Claim a832aaba30ee658e (verified 2026-05-16). https://sourcescore.org/api/v1/claims/a832aaba30ee658e.json

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<iframe src="https://sourcescore.org/embed/claim/a832aaba30ee658e/" width="100%" height="360" frameborder="0" loading="lazy" title="Google Gemma 3 publicly released on: 2025-03-12 by Google DeepMind — Gemma 3 family (1B/4B/12B/27B), 128k context, multimodal vision."></iframe>

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Frequently asked questions

Is the claim "Google Gemma 3 publicly released on: 2025-03-12 by Google DeepMind — Gemma 3 family (1B/4B/12B/27B), 128k context, multimodal vision." 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 "Google Gemma 3 publicly released on: 2025-03-12 by Google DeepMind — Gemma 3 family (1B/4B/12B/27B), 128k context, multimodal vision."?

Evidence comes from 2 primary sources: Google, Google / Hugging Face. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/a832aaba30ee658e.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/a832aaba30ee658e.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.

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cURL

curl https://sourcescore.org/api/v1/claims/a832aaba30ee658e.json

JavaScript / TypeScript

const r = await fetch("https://sourcescore.org/api/v1/claims/a832aaba30ee658e.json"); const envelope = await r.json(); console.log(envelope.claim.statement); // "Google Gemma 3 publicly released on: 2025-03-12 by Google DeepMind — Gemma 3 family (1B/4B/12B/27B), 128k context, multimodal vision."

Python

import httpx r = httpx.get("https://sourcescore.org/api/v1/claims/a832aaba30ee658e.json") envelope = r.json() print(envelope["claim"]["statement"]) # "Google Gemma 3 publicly released on: 2025-03-12 by Google DeepMind — Gemma 3 family (1B/4B/12B/27B), 128k context, multimodal vision."

LangChain (retrieve-then-cite)

from langchain_core.tools import tool import httpx @tool def get_google_gemma_3_fact() -> dict: """Fetch the verified SourceScore claim for Google Gemma 3.""" r = httpx.get("https://sourcescore.org/api/v1/claims/a832aaba30ee658e.json") return r.json()