OpenAI text-embedding-3-large

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

VdelaveRazpoložljivo

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.

Preizkusi Pridobi API ključ Primerjaj

Cena: 0,156 $/1M vhod
Kontekst: 8191 žetonov
Vhod → Izhod: Besedilo → Vektor
Razvijalec: OpenAI
Posodobljeno: 23. september 2026

Igrišče

Preizkusi OpenAI text-embedding-3-large

Vnos in rezultat

0,156 $/1M vhod

Vhod

0 / 32.000

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O OpenAI text-embedding-3-large

PovzetekOd 23. september 2026

OpenAI text-embedding-3-large je model razvijalca OpenAI iz kategorije Vdelave. Na Railwail stane OpenAI text-embedding-3-large 0,156 $ / 1M vhodnih žetonov. Kontekstno okno vsebuje 8191 žetonov.

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.

Ozadje

O OpenAI

Ustanovljeno 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.

Obiščite 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: 8191 žetonov

Zmožnosti

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

Usposabljanje in 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.

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

Znane omejitve

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

Raziskovalni članki

Cene

Cene v ameriških dolarjih. Uporaba se obračuna iz vnaprej plačanih kreditov.

Vhod	0,156 $ / 1M žetonov

Obračunani so žetoni, ki jih vsak zahtevek dejansko porabi.
1 kredit = 0,01 $

Kalkulator stroškov

Kalkulator cen

Vhodnih žetonov

/ zapr.

Izhodnih žetonov

/ zapr.

Zahtevki

Skupaj

0,02 $

2 kreditov

Na zahtevek

0,0002 $ · 0,02 kreditov

Vsak zahtevek se zaokroži navzgor na 0,01 kredita.

API

Pokličite OpenAI text-embedding-3-large s svojim Railwail API ključem. V zahtevku uporabite to ID modela:

text-embedding-3-largeDokumentacija API Pridobite 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);

Nastavite ključ kot RAILWAIL_API_KEYUstvari API ključ

Specifikacije

ID modela: text-embedding-3-large
Razvijalec: OpenAI
Kategorija: Vdelave
Vhod: Besedilo
Izhod: Vektor
Kontekstno okno: 8191 žetonov
Obračunavanje: Po porabi (žetoni ali čas GPU)
Velikost modela: Undisclosed
Licenca: Proprietary commercial API. Generated embeddings may be stored and used commercially under the OpenAI Usage Policy.
Kataložni vnos posodobljen: 23. september 2026

Vhodne parametre

Vhodi in nastavitve iz vhodne sheme modela. Primer v razdelku API prikazuje, katere od njih API sprejema.

ParameterTipPrivzetoDovoljene vrednosti

inputObvezno
Text to embed (single string or array of strings)
Tip: Besedilo
Privzeto: –
Dovoljene vrednosti: Do 32.000 znakov
dimensions
Tip: Celo število
Privzeto: 3072
Dovoljene vrednosti: 256 do 3072
encoding_format
Tip: Izbira
Privzeto: float
Dovoljene vrednosti: float ali base64

Oznake

openai
embedding
retrieval
rag
matryoshka
multilingual

Primeri pozivov

Primeri iz kataloga Railwail. Niso bili generirani v živo na tej strani.

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Primeri uporabe

Kaj se uporablja za

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 vodniki

Embedding Models for Search & Science

Vsi modeli v Vdelave

Pogosta vprašanja

Kaj je OpenAI text-embedding-3-large?

OpenAI text-embedding-3-large je model podjetja OpenAI iz kategorije Vdelave. Na Railwailu ga lahko pokličete z ključem API prek Railwailovega API-ja.

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

Na Railwail stane OpenAI text-embedding-3-large 0,156 $ / 1M vhodnih žetonov. Zaračunano vam je tisto, kar vsaka zahteva dejansko porabi. Uporaba se plača s predplačanimi krediti; 1 kredit je enak 0,01 $.

Kako veliko je kontekstno okno OpenAI text-embedding-3-large?

Kontekstno okno OpenAI text-embedding-3-large vsebuje 8191 žetonov.

Kako hiter je OpenAI text-embedding-3-large?

Za OpenAI text-embedding-3-large je na Railwailu še premalo izmerjenih zagonov, da bi navedli čas izvajanja. Odvisno je od vnosa, nastavitev in obremenitve pri ponudniku.

Je OpenAI text-embedding-3-large boljši od OpenAI text-embedding-3-small?

To je odvisno od naloge. OpenAI text-embedding-3-large (OpenAI) in OpenAI text-embedding-3-small (OpenAI) sta oba modela iz kategorije Vdelave. Stran za primerjavo prikazuje njune cene in specifikacije drug ob drugem.

Primerjaj OpenAI text-embedding-3-large in OpenAI text-embedding-3-small

Kako uporabljam OpenAI text-embedding-3-large prek API-ja?

Ustvarite ključ Railwailovega API-ja in pošljite zahtevo z ID-jem modela text-embedding-3-large. Primeri kode za curl, Python in JavaScript so v razdelku API na tej strani.

Primerljivi modeli

Vsi v tej 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 $/1M vhod
85 % cenejše na enoto
Primerjaj OpenAI text-embedding-3-large in OpenAI text-embedding-3-small

Uporabite OpenAI text-embedding-3-large prek API

En API ključ za vsak model na Railwail. Uporaba se obračuna iz vnaprej plačanih kreditov, 1 kredit = 0,01 $.

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