OpenAI text-embedding-3-large

Name: OpenAI text-embedding-3-large
Brand: OpenAI
SKU: text-embedding-3-large
Price: 0.156 USD
Availability: InStock

EmbeddingsTilgængelig

af OpenAItext-embedding-3-large

OpenAI's highest-quality embedding model. Returns 3072-dim vectors by default and supports reducing dimensions via the dimensions parameter. Outperforms text-embedding-3-small and the older ada-002 on MTEB and multilingual MIRACL retrieval benchmarks, for cases where accuracy matters more than cost.

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Pris: 0,156 US$/1M in
Kontekst: 8.191 tokens
Input → output: Tekst → Vektor
Udvikler: OpenAI
Opdateret: 23. september 2026

Playground

Prøv OpenAI text-embedding-3-large

Input & resultat

0,156 US$/1M in

Input

0 / 32.000

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Resultat

Vektoren vises her.

Denne kørsel

Prisen vises, når inputtet er fuldstændigt.

Ny her?

10 gratis credits (0,10 US$) når du tilmelder dig med Google

Kan bruges 24 timer efter tilmelding, op til 5 kørsler pr. dag og højst 2 credits pr. kørsel. Andre login-metoder starter uden credits.

Om OpenAI text-embedding-3-large

Kort sagtFra 23. september 2026

OpenAI text-embedding-3-large er en model af OpenAI i kategorien Embeddings. På Railwail koster OpenAI text-embedding-3-large 0,156 US$ / 1M input-tokens. Kontekstvinduet indeholder 8.191 tokens.

text-embedding-3-large is OpenAI's most capable third-generation embedding model. It produces 3072-dim embeddings by default and, like the small variant, supports the dimensions parameter for Matryoshka-style truncation so you can store shorter vectors when needed. It leads OpenAI's lineup on the MTEB English benchmark and on the multilingual MIRACL retrieval benchmark, making it the pick when embedding quality is the priority over price. Use it for high-recall semantic search, deduplication and reranking-style retrieval. Accepts a single string or a batch per request.

Baggrund

Om OpenAI

Grundlagt 2015 · San Francisco, California, USA

OpenAI was founded in December 2015 by Sam Altman, Elon Musk, Greg Brockman, Ilya Sutskever, Wojciech Zaremba and John Schulman, and restructured to capped-profit OpenAI LP in 2019. The embedding model family started with text-embedding-ada-001 in 2021, was unified into text-embedding-ada-002 in December 2022 (still the most-used embedding model on Earth at one point) and replaced in January 2024 by text-embedding-3-small and text-embedding-3-large. The v3 release was OpenAI's first to support Matryoshka-style dimension reduction (sale of arbitrary 256-3072 dim vectors from the same model) and beat ada-002 by 20+ percentage points on the MIRACL multilingual retrieval benchmark while costing roughly the same.

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Arkitektur

Transformer bi-encoder with Matryoshka representation learning

OpenAI text-embedding-3-large is the flagship embedding model in the v3 generation. It produces 3,072-dimensional vectors by default with full Matryoshka representation support, so callers can request any dimension between 256 and 3,072 in the API and OpenAI will truncate-and-renormalise without re-running the model. The model accepts up to 8,191 tokens per input and was trained with a contrastive retrieval objective on a curated multilingual web corpus, including search-query/document pairs and large-scale instruction-tuned pairs. It scores 64.6% on MTEB and 54.9% on MIRACL multilingual retrieval, a step up from 61.0% / 31.4% for ada-002. Pricing is $0.00013 per 1k tokens. Output vectors are L2-normalised. Like all OpenAI models the system is closed-source and hosted only. OpenAI has not published a technical paper for the v3 family beyond a launch blog.

Parametre: Undisclosed
Kontekst: 8.191 tokens

Funktioner

3,072-dim vectors with Matryoshka truncation to any 256-3072 size
8,191-token context window for long-document embedding
Multilingual coverage across 100+ languages
State-of-the-art MIRACL multilingual retrieval (~55%)
L2-normalised vectors with cosine similarity
Drop-in upgrade from ada-002 via the same /embeddings endpoint
Best for: production RAG, multilingual search, retrieval-heavy SaaS

Træning og licens

Not disclosed. OpenAI describes a 'curated multilingual web corpus' with search-query/document pairs and instruction-tuned pairs.

Licens: Proprietary commercial API. Generated embeddings may be stored and used commercially under the OpenAI Usage Policy.

Sikkerhedstests: Embeddings are not subject to content-filter constraints; OpenAI publishes general bias and fairness statements for the embeddings endpoint.

Kendte begrænsninger

Closed weights, hosted only
Cannot fine-tune the model
3,072-dim full vectors are storage-heavy without truncation
Worse-than-Cohere v3 on some low-resource languages
8,191-token cap may force chunking for very long documents

Forskningsartikler

Priser

Priser i US-dollar. Forbrug debiteres fra forudbetalte credits.

Input	0,156 US$ / 1M tokens

Faktureres efter de tokens, som hver anmodning faktisk bruger.
1 kredit = 0,01 US$

Omkostningsberegner

Prisberegner

Input-tokens

/ anmodning

Output-tokens

/ anmodning

Anmodninger

I alt

0,02 US$

2 credits

Pr. anmodning

0,0002 US$ · 0,02 credits

Hver anmodning rundes op til 0,01 credits.

API

Kald OpenAI text-embedding-3-large med din Railwail API-nøgle. Brug dette model-ID i anmodningen:

text-embedding-3-largeAPI-dokumentation Få en API-nøgle

curl https://railwail.com/api/v1/embeddings \
  -H "Authorization: Bearer $RAILWAIL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-3-large",
    "input": "The quick brown fox"
  }'

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["RAILWAIL_API_KEY"],
    base_url="https://railwail.com/api/v1",
)

res = client.embeddings.create(
    model="text-embedding-3-large",
    input="The quick brown fox",
)

print(len(res.data[0].embedding))

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.RAILWAIL_API_KEY,
  baseURL: "https://railwail.com/api/v1",
});

const res = await client.embeddings.create({
  model: "text-embedding-3-large",
  input: "The quick brown fox"
});

console.log(res.data[0].embedding.length);

Indstil din nøgle som RAILWAIL_API_KEYOpret API-nøgle

Specifikationer

Model-ID: text-embedding-3-large
Udvikler: OpenAI
Kategori: Embeddings
Input: Tekst
Output: Vektor
Kontekstvindue: 8.191 tokens
Fakturering: Efter forbrug (tokens eller GPU-tid)
Modelstørrelse: Undisclosed
Licens: Proprietary commercial API. Generated embeddings may be stored and used commercially under the OpenAI Usage Policy.
Katalogelement opdateret: 23. september 2026

Inputparametre

Inputs og indstillinger fra modellens inputskema. Eksemplet i API-afsnittet viser, hvilke af dem API'en accepterer.

ParameterTypeStandardTilladte værdier

inputpåkrævet
Text to embed (single string or array of strings)
Type: Tekst
Standard: –
Tilladte værdier: op til 32.000 tegn
dimensions
Type: Heltal
Standard: 3072
Tilladte værdier: 256 til 3.072
encoding_format
Type: Valg
Standard: float
Tilladte værdier: float eller base64

Eksempelprompts

Eksempler fra Railwail-kataloget. De blev ikke genereret live på denne side.

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Anvendelsestilfælde

Hvad det bruges til

Production RAG for ChatGPT-style assistants
Multilingual enterprise search
Semantic deduplication and clustering at scale
Recommendation systems based on text similarity
Embeddings for downstream classification tasks

Relaterede vejledninger

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Ofte stillede spørgsmål

Hvad er OpenAI text-embedding-3-large?

OpenAI text-embedding-3-large er en model fra OpenAI i kategorien Embeddings. På Railwail kan du kalde den med en API-nøgle via Railwail API.

Hvad koster OpenAI text-embedding-3-large på Railwail?

På Railwail koster OpenAI text-embedding-3-large 0,156 US$ / 1M input-tokens. Du betaler for det, som hver anmodning faktisk bruger. Forbrug betales fra forudbetalte credits; 1 credit svarer til 0,01 US$.

Hvad er kontekstvinduet for OpenAI text-embedding-3-large?

Kontekstvinduet for OpenAI text-embedding-3-large indeholder 8.191 tokens.

Hvor hurtig er OpenAI text-embedding-3-large?

Der er endnu ikke nok målte kørsler af OpenAI text-embedding-3-large på Railwail til at angive en udførelsestid. Det afhænger af inputtet, indstillingerne og belastningen hos provideren.

Er OpenAI text-embedding-3-large bedre end OpenAI text-embedding-3-small?

Det afhænger af opgaven. OpenAI text-embedding-3-large (OpenAI) og OpenAI text-embedding-3-small (OpenAI) er begge modeller i kategorien Embeddings. Sammenligningssiden viser deres priser og specifikationer side om side.

Sammenlign OpenAI text-embedding-3-large og OpenAI text-embedding-3-small

Hvordan bruger jeg OpenAI text-embedding-3-large via API'en?

Opret en Railwail API-nøgle og send din anmodning med model-ID'en text-embedding-3-large. Kodeeksempler til curl, Python og JavaScript findes i API-sektionen på denne side.

Sammenlignelige modeller

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OpenAI text-embedding-3-smallOpenAI
OpenAI's small, low-cost embedding model. Returns 1536-dim vectors by default and supports shortening output dimensions via the dimensions parameter without retraining. Replaced text-embedding-ada-002 with better retrieval quality at a fraction of the price, and is the default choice for general-purpose semantic search and RAG.
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Sammenlign OpenAI text-embedding-3-large og OpenAI text-embedding-3-small

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