Verified claim · AI-ML · 100% confidence
GPT-3 parameter count: 175000000000.
Last verified 2026-05-16 · Methodology veritas-v0.1 · 1ca2cc2864dfb376
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Structured fields
- Subject
- GPT-3
- Predicate
parameter_count- Object
- 175000000000
- Confidence
- 100%
- Tags
- gpt-3 · openai · parameters · 175b · brown · 2020
Sources (2)
[1] preprint · OpenAI / arXiv (Brown et al.) · 2020-05-28
Language Models are Few-Shot Learners“We train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model.”
[2] official blog · OpenAI · 2021-03-25
GPT-3 Powers the Next Generation of AppsOpenAI is rated by SourceScore — see its reliability →
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Related claims
Other verified claims sharing tags with this one — useful for LLM retrieval graphs and citation discovery.
GPT-3 introduced in paper: Language Models are Few-Shot Learners (Brown et al., 2020).
7d3e6a39b1656571 · 100% confidence · shares 3 tags (gpt-3, openai, 2020)
Kaplan scaling laws introduced in paper: Kaplan et al. 2020 — Scaling Laws for Neural Language Models.
22e12bfbe7770657 · 100% confidence · shares 2 tags (openai, 2020)
Retrieval-Augmented Generation (RAG) introduced in paper: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020).
d15057ced937a103 · 100% confidence · shares 1 tag (2020)
InstructGPT methodology introduced in paper: Training language models to follow instructions with human feedback (Ouyang et al., 2022).
5da8f8dffc038b8e · 100% confidence · shares 1 tag (openai)
CLIP (Contrastive Language-Image Pretraining) introduced in paper: Learning Transferable Visual Models From Natural Language Supervision (Radford et al., 2021).
85a3ca745eaf4ee0 · 100% confidence · shares 1 tag (openai)
Frequently asked questions
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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 "GPT-3 parameter count: 175000000000."?
Evidence comes from 2 primary sources: OpenAI / arXiv (Brown et al.), OpenAI. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/1ca2cc2864dfb376.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.
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cURL
curl https://sourcescore.org/api/v1/claims/1ca2cc2864dfb376.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/1ca2cc2864dfb376.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "GPT-3 parameter count: 175000000000."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/1ca2cc2864dfb376.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "GPT-3 parameter count: 175000000000."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_gpt_3_fact() -> dict:
"""Fetch the verified SourceScore claim for GPT-3."""
r = httpx.get("https://sourcescore.org/api/v1/claims/1ca2cc2864dfb376.json")
return r.json()