Does Azure OpenAI Text Embedding 3 Large support streaming?

Streaming depends on the active provider; check the providers table on this page for flags.

Can Azure OpenAI Text Embedding 3 Large generate paragraphs of text?

No. Azure OpenAI Text Embedding 3 Large returns embeddings (vectors). Pair it with a chat model if you need generated language.

How do I call Azure OpenAI Text Embedding 3 Large via API?

Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "azure/text-embedding-3-large" and your LLM.API key. See the code snippet on this page.

Can I use tools or structured outputs with Azure OpenAI Text Embedding 3 Large?

Tooling support varies; for pure embedding models, prefer the modalities listed rather than assuming chat tools.

What are limitations of Azure OpenAI Text Embedding 3 Large?

Like other API models, Azure OpenAI Text Embedding 3 Large can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.

When should I choose Azure OpenAI Text Embedding 3 Large?

You need consistent vectors from an OpenAI-compatible embeddings API — especially when you specifically need Azure OpenAI Text Embedding 3 Large.

Where is the canonical page for Azure OpenAI Text Embedding 3 Large?

https://llmapi.ai/models/azure-text-embedding-3-large/

How is Azure OpenAI Text Embedding 3 Large priced on LLM.API?

Listed pricing metadata shows: In $0.13 / 1M tokens. LLM.API may offer discounted effective rates (illustrative ~30% callout vs list when available).. Confirm live rates in the LLM.API dashboard or docs before production budgeting.

Is Azure OpenAI Text Embedding 3 Large a chat model?

No—Azure OpenAI Text Embedding 3 Large is categorized as a embedding model. Use the matching API surface rather than assuming chat completions.

What modalities does Azure OpenAI Text Embedding 3 Large support?

Azure OpenAI Text Embedding 3 Large accepts text and produces embedding according to its architecture metadata on LLM.API.

What is the context length for Azure OpenAI Text Embedding 3 Large?

Reported context for Azure OpenAI Text Embedding 3 Large is 8K tokens. Always verify the active provider row if multiple providers are listed.

Azure OpenAI Text Embedding 3 Large

Up to 30%

Embed text at scale with Azure OpenAI Text Embedding 3 Large on LLM.API—ideal for search, RAG, and clustering via the OpenAI-compatible embeddings API.

Start Using API

What is Azure OpenAI Text Embedding 3 Large?

Choose Azure OpenAI Text Embedding 3 Large when your pipeline needs dense representations instead of generated prose. Azure-hosted OpenAI text-embedding-3-large. Provisional: requires the Azure deployment to be named exactly `text-embedding-3-large` until ProviderKeyOptions.AzureDeploymentType plumbing lands for embeddings. 3072-dim default, Matryoshka shrinkable to 256+. LLM.API exposes it with OpenAI-compatible embedding calls.

Providers

LLM.API routes Azure OpenAI Text Embedding 3 Large to the providers below, with discounted effective rates versus list price.

List price by provider ($ / 1M tokens)

InputOutput

azure$0.13 in

Provider list prices; the LLM.API discount applies on top.

Provider	Pricing	Context	Capabilities
azure30% off	in $0.13/1M	8K tokens	—

Try this model

Test Azure OpenAI Text Embedding 3 Large right here — free to start.

Azure OpenAI Text Embedding 3 Large

Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call Azure OpenAI Text Embedding 3 Large through the OpenAI-compatible API — POST /v1/embeddings.

python

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAP_KEY",
    base_url="https://api.llmapi.ai/v1",
)

resp = client.embeddings.create(
    model="azure/text-embedding-3-large",
    input="Semantic search starts with strong embeddings.",
)
print(len(resp.data[0].embedding))

{
  "model": "azure/text-embedding-3-large",
  "input": "Semantic search starts with strong embeddings."
}

5 Core Capabilities

Semantic vectorization
Maps text into dense vectors for similarity search and clustering.
Retrieval readiness
Fits RAG pipelines that need stable document and query embeddings.
Low-latency lookups
Suitable for online retrieval when paired with a vector store. Reflects OpenAI positioning for this endpoint.
Multilingual text
Embeds content across languages depending on the underlying model family. Tuned to how teams typically call Azure OpenAI Text Embedding 3 Large.
Cross-document matching
Supports near-duplicate detection and topical grouping. Reflects OpenAI positioning for this endpoint.

6 Most Valuable Use Cases

Semantic search over docs, tickets, and wikis with Azure OpenAI Text Embedding 3 Large
Recommendation features based on text similarity
Anomaly grouping in incident descriptions with Azure OpenAI Text Embedding 3 Large
Lead and account similarity scoring from notes
RAG retrieval for grounded chat answers with Azure OpenAI Text Embedding 3 Large
Duplicate and near-duplicate content detection

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

Intelligent AI Routing

Automatically route each request to the best model across providers based on latency, cost, and quality—without changing your integration or redeploying code.
One endpoint, every model.
Cost-Aware Execution

Control spend with per-request cost estimation, smart model selection, and centralized quotas so teams can experiment fast without runaway bills or manual tracking.
More performance, less spend.
Resilient Fallback Flows

Define automatic, provider-agnostic fallbacks to keep your app up during outages, rate limits, or timeouts—no brittle failover logic scattered through your codebase.
Never go dark on users.
Deep LLM Observability

Trace every call across providers with logs, metrics, and request replay so you can debug, tune prompts, and optimize model choices from one unified dashboard.
See every token, everywhere.
Task-Level Orchestration

Describe tasks, not models. LLM.API maps them to the right tools, models, and prompts so you ship complex AI workflows with minimal glue code.
Think tasks, not models.
High-Throughput Batch APIs

Process millions of inferences efficiently with optimized batch pipelines, concurrency controls, and retry logic—all behind the same simple interface you use for single calls.
Scale from 1 to millions.

Why run Azure OpenAI Text Embedding 3 Large on LLM.API?

Unified AI Routing
Practical: Reach Azure OpenAI Text Embedding 3 Large and sibling models through one OpenAI-compatible endpoint.
Cost Control
Production: Compare provider price points and keep spend visible as you scale Azure OpenAI Text Embedding 3 Large.
Reliability Layer
Production: Retry and route across configured providers when a single upstream blips.
Observability
Practical: Trace prompts, tokens, and errors for Azure OpenAI Text Embedding 3 Large alongside the rest of your stack.
Drop-in SDKs
Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Model Breadth
Swap Azure OpenAI Text Embedding 3 Large for chat, media, or embedding alternatives without rewriting auth.

When to Use — When NOT to Use

Use it if...

You need consistent vectors from an OpenAI-compatible embeddings API (Azure OpenAI Text Embedding 3 Large)
Throughput and cost matter more than generative prose quality (Azure OpenAI Text Embedding 3 Large)
You are building search, RAG, or clustering on text (Azure OpenAI Text Embedding 3 Large)

Avoid if...

You only need OCR or speech features
You need generative answers instead of vectors
You require cross-model vector compatibility without re-indexing

RELIABILITY