opendunes

Titan Text Embeddings V2

New

by AmazonReleased 26 October 2026

This model takes in plain text of about 8.1 pages at a time and gives back a numeric code for it, not written words. A buyer sits down with it to sort a large pile of articles or reports by subject, or to match a question to the right passage in a long file. Unlike the picture-matching model in its family, it takes in no photos or captions and is built for whole long documents. It stops where a job needs a written reply, since it only produces codes that computers compare, so that kind of work needs a different kind of model.

TakesText
GivesEmbeddings
Create API keyCompare

What it costs

You pay for what each request uses. No subscription, no minimum.

≈ 0,0067 DA per page3 000 DA buys about 450 000 pages

One page, the ordinary use

1 000 tokens sent× 6,66 DA per 1M0,0067 DA
Total≈ 0,0067 DA

Every rate

Meter↑ you send↓ comes back
Text tokens6,66 DA

per 1M tokens

Coding a three-page memo for a quick topic check≈ 0,020 DA
Coding one full-length report of about eight pages in a single pass≈ 0,054 DA
When you pay
After the answer, from your balance. Nothing is held.
Why it varies
The length of the text.
After each one
The exact charge shows on the request.
DepositDeposits from 50 DA, with Edahabia or CIB.

Where your request runs

One company runs this model. OpenDunes sends every request to it. DA per 1M tokens: ↑ what you send.

Amazon Bedrock…
↑ text
6,66 DA
Latency
…
Provider↑ textLatencyUptime
Amazon Bedrock6,66 DA……

You pay the price of the provider that served your request. Prices read 15:18 · speed over the last 30 minutes · uptime over 3 days.

Call it in code

Swap the base URL and the key. Your OpenAI code runs unchanged.

POST /v1/embeddings
Base URL
https://opendunes.com/v1
Model ID
amazon/titan-embed-text-v2
import os
from openai import OpenAI

client = OpenAI(
    base_url="https://opendunes.com/v1",
    api_key=os.environ["OPENDUNES_API_KEY"],
)

resp = client.embeddings.create(
    model="amazon/titan-embed-text-v2",
    input="Delivery to Oran takes two days.",
)

print(resp.data[0].embedding[:5])
pip install openaiFull reference for this route
Sign in to run it

Runs this request once on your balance.

Questions about Titan Text Embeddings V2

  • How much does Titan Text Embeddings V2 cost in dinars?

    About 0,0067 DA per page. ↑ 6,66 DA per 1M tokens.

  • Is Titan Text Embeddings V2 free? Can I try it?

    No. You pay per use from your balance, with no subscription. Run it once from Call it in code, at the same price.

  • What is Titan Text Embeddings V2 best for?

    This model takes in plain text of about 8.1 pages at a time and gives back a numeric code for it, not written words. A buyer sits down with it to sort a large pile of articles or reports by subject, or to match a question to the right passage in a long file. Unlike the picture-matching model in its family, it takes in no photos or captions and is built for whole long documents. It stops where a job needs a written reply, since it only produces codes that computers compare, so that kind of work needs a different kind of model.

  • Titan Text Embeddings V2 or Titan Multimodal Embeddings G1?

    Titan Multimodal Embeddings G1 costs 0,26 DA more for the same page.

  • How do I pay for Titan Text Embeddings V2 without an international card?

    Deposit dinars with your Edahabia or CIB card. Every model, Titan Text Embeddings V2 included, is paid from that balance. No foreign card, no conversion.

  • Does Titan Text Embeddings V2 work in Arabic, French or Darja?

    Amazon names no languages for Titan Text Embeddings V2. Arabic, French and Darja: not stated.

  • How much text in one input?

    Up to 8 192 tokens. Split anything longer.

  • How many dimensions?

    1 024

  • Who makes Titan Text Embeddings V2, and when was it released?

    Amazon. Released 26 October 2026.

Other Amazon models, priced in dinars

See all Amazon models
Titan Embeddings G1 - TextAmazon · Oct 26, 2026

This model takes a piece of text of up to about eight pages and turns it into a numeric code that computers can compare, so it finds and groups similar writing rather than writing anything itself. Sit down with it to sort thousands of documents by topic, power a search that matches a question to the right passage, or flag near-duplicate text. Unlike its family's picture-matching sibling, which reads only a caption's worth of text, it digests full long documents in one go. It never composes answers or prose, so any job that needs a written reply needs a different kind of model.

8 pages per call
≈0,033 DAper page
View model
Titan Multimodal Embeddings G1Amazon · Oct 26, 2026

This model takes a short piece of text, a picture, or both together, and turns them into a matching code that computers can compare. It reads about a tenth of a page at a time, so it suits short captions, product names, and single photos rather than long documents. Its strength is finding things that are alike: a shopper's words can be matched to product photos, or one picture to similar pictures, because text and images end up in the same shared space. It does not write text or answers, so any job that needs a written reply needs a different kind of model.

≈0,27 DAper page
View model
Nova Micro 1.0Amazon · Dec 5, 2024

Amazon Nova Micro 1.0 takes text and gives back text, and it is the quickest to respond in the Nova family, reading up to about 128 pages. Send it text to summarise, translate, sort or brainstorm over, or a quick chat turn; it handles simple maths and basic code. Replies stop at about 5 pages.

Uses your tools120 pages at once
≈0,035 DAper message
View model
Nova 2 LiteAmazon · Dec 2, 2025

Nova 2 Lite reads text, images, video and files, answers in text, can think step by step before replying, and keeps about 1,000 pages in mind at once. Give it documents to pull facts from, video to extract details from, code to write, or a multi-step task that uses the tools you connect to it. Nova Lite 1.0 reads only text and images, about 300 pages, and Nova Premier handles the hardest reasoning.

Thinks firstUses your tools1 000 pages at once
≈0,52 DAper message
View model