OpenAI text-embedding-3-large

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

EmbeddingsDostupno

od 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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Cijena: 0,156 US$/1M in
Kontekst: 8.191 tokena
Ulaz → izlaz: Tekst → Vektor
Razvojni inženjer: OpenAI
Ažurirano: 23. septembar 2026.

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Unos i rezultat

0,156 US$/1M in

Unos

0 / 32.000

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O OpenAI text-embedding-3-large

UkratkoStanje: 23. septembar 2026.

OpenAI text-embedding-3-large je model od OpenAI iz kategorije Embeddings. Na Railwailu, OpenAI text-embedding-3-large košta 0,156 US$ / 1M input tokena. Kontekstni prozor sadrži 8.191 tokena.

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.

Pozadina

O OpenAI

Osnovano 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.

Posjetite OpenAI

Arhitektura

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.

Parametri: Undisclosed
Kontekst: 8.191 tokena

Mogućnosti

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

Obuka i licenca

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

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

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

Poznata ograničenja

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

Znanstveni članci

Cijene

Cijene u USD-u. Korištenje se naplaćuje iz unaprijed plaćenih kredita.

Ulaz	0,156 US$ / 1M tokena

Naplaćuje se prema tokenima koje svaki zahtjev zapravo koristi.
1 kredit = 0,01 US$

Kalkulator troškova

Kalkulator cijena

Ulazni tokeni

/ zahtjev

Izlazni tokeni

/ zahtjev

Zahtjevi

Ukupno

0,02 US$

2 kredita

Po zahtjevu

0,0002 US$ · 0,02 kredita

Svaki zahtjev se zaokružuje na 0,01 kredita.

API

Pozovi OpenAI text-embedding-3-large sa svojim Railwail API ključem. Koristi ovaj ID modela u zahtjevu:

text-embedding-3-largePogledaj API dokumentaciju Dobij API ključ

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);

Postavite ključ kao RAILWAIL_API_KEYKreiraj API ključ

Specifikacije

ID modela: text-embedding-3-large
Razvojni inženjer: OpenAI
Kategorija: Embeddings
Ulaz: Tekst
Izlaz: Vektor
Kontekstni prozor: 8.191 tokena
Naplata: Po potrošnji (tokeni ili GPU vrijeme)
Veličina modela: Undisclosed
Licenca: Proprietary commercial API. Generated embeddings may be stored and used commercially under the OpenAI Usage Policy.
Katalog ažuriran: 23. septembar 2026.

Parametri ulaza

Ulazi i postavke iz sheme ulaza modela. Primjer u odjeljku API pokazuje koje od njih API prihvaća.

ParametarTipZadanoDozvoljene vrijednosti

inputobavezno
Text to embed (single string or array of strings)
Tip: Tekst
Zadano: –
Dozvoljene vrijednosti: do 32.000 znakova
dimensions
Tip: Cijeli broj
Zadano: 3072
Dozvoljene vrijednosti: 256 do 3.072
encoding_format
Tip: Izbor
Zadano: float
Dozvoljene vrijednosti: float ili base64

Oznake

openai
embedding
retrieval
rag
matryoshka
multilingual

Primjer promptova

Primjeri iz Railwail kataloga. Nisu generirani uživo na ovoj stranici.

FAQ Matching
How do I reset my password?
Code Search
React useEffect cleanup function memory leak

Primjena

Za što se koristi

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

Povezani vodiči

Embedding Models for Search & Science

Svi modeli: Embeddings

Često postavljana pitanja

Što je OpenAI text-embedding-3-large?

OpenAI text-embedding-3-large je model od OpenAI iz kategorije Embeddings. Na Railwailu ga možete pozvati s API ključem kroz Railwail API.

Koliko stoji OpenAI text-embedding-3-large na Railwailu?

Na Railwailu, OpenAI text-embedding-3-large košta 0,156 US$ / 1M input tokena. Naplaćuje se ono što svaki zahtjev zapravo koristi. Korištenje se plaća iz unaprijed kupljenih kredita; 1 kredit jednaka je 0,01 US$.

Koliki je kontekstni prozor od OpenAI text-embedding-3-large?

Kontekstni prozor od OpenAI text-embedding-3-large sadrži 8.191 tokena.

Koliko je brz OpenAI text-embedding-3-large?

Za OpenAI text-embedding-3-large na Railwailu još nema dovoljno izmjerenih pokretanja da bi se navedlo vrijeme izvršavanja. Ovisi o unošenju, postavkama i opterećenju kod pružatelja.

Je li OpenAI text-embedding-3-large bolji od OpenAI text-embedding-3-small?

To ovisi o zadatku. OpenAI text-embedding-3-large (OpenAI) i OpenAI text-embedding-3-small (OpenAI) su oba modela iz kategorije Embeddings. Stranica za usporedbu prikazuje njihove cijene i specifikacije usporedno.

Usporedi OpenAI text-embedding-3-large i OpenAI text-embedding-3-small

Kako koristim OpenAI text-embedding-3-large kroz API?

Kreiraj Railwail API ključ i pošalji svoj zahtjev s ID-om modela text-embedding-3-large. Primjeri koda za curl, Python i JavaScript nalaze se u odjeljku API na ovoj stranici.

Usporedivi modeli

Svi u ovoj kategoriji

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.
0,024 US$/1M in
85 % jeftinije po jedinici
Usporedi OpenAI text-embedding-3-large i OpenAI text-embedding-3-small

Koristi OpenAI text-embedding-3-large preko API-ja

Jedan API ključ za sve modele na Railwailu. Korištenje se naplaćuje iz unaprijed plaćenih kredita, 1 kredit = 0,01 US$.

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