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

Listed pricing metadata shows: In $0.02 / 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 Small a chat model?

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

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

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

What modalities does Azure OpenAI Text Embedding 3 Small support?

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

Does Azure OpenAI Text Embedding 3 Small support streaming?

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

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

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

What are limitations of Azure OpenAI Text Embedding 3 Small?

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

Which endpoint do I call for Azure OpenAI Text Embedding 3 Small?

POST https://api.llmapi.ai/v1/chat/completions with model "azure/text-embedding-3-small" and your LLM.API key. The request and response bodies are OpenAI-compatible.

How do I switch from OpenAI to Azure OpenAI Text Embedding 3 Small on LLM.API?

Change two lines: set base_url to https://api.llmapi.ai/v1 and api_key to your LLM.API key. The rest of your OpenAI SDK code stays the same.

Does Azure OpenAI Text Embedding 3 Small support function calling and structured outputs?

Function calling: not supported. Structured outputs: not supported.

Azure OpenAI Text Embedding 3 Small API: Endpoint & Pricing

Azure OpenAI Text Embedding 3 Small delivers OpenAI embeddings through LLM.API so retrieval stacks stay simple and portable.

Start Using API

What is Azure OpenAI Text Embedding 3 Small?

Azure OpenAI Text Embedding 3 Small is an embedding model on LLM.API (`azure/text-embedding-3-small`). Azure-hosted OpenAI text-embedding-3-small. Provisional: requires the Azure deployment to be named exactly `text-embedding-3-small` until ProviderKeyOptions.AzureDeploymentType plumbing lands for embeddings. 1536-dim default, Matryoshka shrinkable. It maps text into embedding vectors for retrieval, clustering, and semantic search—not for chat completions.

Can you self-host Azure OpenAI Text Embedding 3 Small?

No. We found no public weights for Azure OpenAI Text Embedding 3 Small on Hugging Face, so there is nothing to download, quantize or serve on your own GPUs: no VRAM budget, no vLLM or GGUF build to plan for. The only way to run it is over an API.

Skip the deploy — use LLM.API as your endpoint

No GPUs, no quantization trade-offs. Same model, OpenAI-compatible, with custom volume discounts on request.

Get API key See API code ↓

Providers

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

List price by provider

InputOutput

azure$0.02 in

$0 out

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

Provider	Pricing	Context	Capabilities
azure	in $0.02; out $0 per 1M tokens	8K tokens	—

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 11 Oct 2026.

Try this model

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

Azure OpenAI Text Embedding 3 Small

Hi! Want to test the model?

Suggestions for your first prompt

Switching from OpenAI? Change 2 lines.

- base_url="https://api.openai.com/v1"
- api_key="YOUR_OPENAI_KEY"
+ base_url="https://api.llmapi.ai/v1"
+ api_key="YOUR_LLMAPI_KEY"

Everything else stays the same — SDK, message format, tools, streaming. Set model to azure/text-embedding-3-small and the call runs unchanged.

bash

curl https://api.llmapi.ai/v1/chat/completions \
  -H "Authorization: Bearer $LLMAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "azure/text-embedding-3-small",
    "messages": [
      {"role": "user", "content": "Give me three crisp launch checklist items."}
    ]
  }'

from openai import OpenAI

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

resp = client.chat.completions.create(
    model="azure/text-embedding-3-small",
    messages=[
        {"role": "system", "content": "You are a precise product assistant."},
        {"role": "user", "content": "Give me three crisp launch checklist items."},
    ],
)
print(resp.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.LLMAPI_KEY,
  baseURL: "https://api.llmapi.ai/v1",
});

const resp = await client.chat.completions.create({
  model: "azure/text-embedding-3-small",
  messages: [
    { role: "user", content: "Give me three crisp launch checklist items." },
  ],
});

console.log(resp.choices[0].message.content);

stream = client.chat.completions.create(
    model="azure/text-embedding-3-small",
    messages=[{"role": "user", "content": "Write a haiku about latency."}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="", flush=True)

{
  "model": "azure/text-embedding-3-small",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ]
}

ENDPOINTS

Supported endpoints & features

Base URL https://api.llmapi.ai/v1 · model ID azure/text-embedding-3-small · OpenAI-compatible.

POST /v1/chat/completionsChat Completions — supported

Feature	Status	How to set it
Function calling (tools)	Not supported	`tools, tool_choice`
Parallel tool calls	Not supported	`parallel_tool_calls`
JSON mode	Not supported	`response_format: {"type": "json_object"}`
Structured outputs	Not supported	`response_format: {"type": "json_schema", …}`
Streaming	Not supported	`stream: true`
Vision (image input)	Not supported	`image_url content parts`
Web search	Not supported	`web_search`

ERRORS

Errors & fallback routing

Code	What it means	What to do
`401`	Missing or invalid API key.	Send `Authorization: Bearer YOUR_LLMAPI_KEY` and check the key is active.
`429`	Rate limited, or the upstream provider is throttling the request.	Back off and retry with jitter; the gateway also retries the request on another provider where one is available.
`5xx`	Upstream provider error or timeout.	Retry; fallback routing sends the retry to the next healthy provider for this model.

When a provider fails or throttles, the request is routed to the next provider serving this model — the ones listed in the providers table above.

Azure OpenAI Text Embedding 3 Small in production

Best workloadsLLM.API features

What the model is good at.

Cross-document matching
Supports near-duplicate detection and topical grouping. Tuned to how teams typically call Azure OpenAI Text Embedding 3 Small.
Low-latency lookups
Suitable for online retrieval when paired with a vector store. Reflects OpenAI positioning for this endpoint.
Semantic vectorization
Maps text into dense vectors for similarity search and clustering. Grounded in the model's embedding role rather than generic chat claims.
Batch throughput
Designed for embedding large corpora via the OpenAI-compatible API. Reflects OpenAI positioning for this endpoint.
Corpus analytics
Enables semantic exploration of knowledge bases and tickets.

What you get when you call Azure OpenAI Text Embedding 3 Small through LLM.API.

Unified AI Routing
Practical: Reach Azure OpenAI Text Embedding 3 Small and sibling models through one OpenAI-compatible endpoint.
One endpoint, every model.
Cost Control
Compare provider price points and keep spend visible as you scale Azure OpenAI Text Embedding 3 Small.
More performance, less spend.
Reliability Layer
Retry and route across configured providers when a single upstream blips.
Never go dark on users.
Observability
Trace prompts, tokens, and errors for Azure OpenAI Text Embedding 3 Small alongside the rest of your stack.
See every token, everywhere.
Drop-in SDKs
Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Think tasks, not models.
Model Breadth
Production: Swap Azure OpenAI Text Embedding 3 Small for chat, media, or embedding alternatives without rewriting auth.
Scale from 1 to millions.

When to Use — When NOT to Use

Use it if...

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

Avoid if...

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

RELIABILITY

Azure OpenAI Text Embedding 3 Small uptime, last 30 days

30-Day Uptime

96.70%

Past Incidents (30d)

Error rate (24h)

9.27%

Last 30 days

26/30 days operational | 96.70% uptime

Availability tracked for the OpenAI GPT API. Full history on the LLM Uptime Status page or the status hub. LLM.API routes around provider outages automatically.

LLM API rating for Azure OpenAI Text Embedding 3 Small

★ 3.7/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & valueEstimated from published specs

4.8Production adoptionDe-facto default embedding model

2.8Benchmark qualityNo public Arena rank — scored on declared reasoning support

2.4Developer ecosystemContext window, tool and JSON support, price against the category median

How is this rating calculated?

Every model gets a separate 1.0–5.0 score for each category it can be used in, so a model can rate high for coding and lower for vision. The score combines three parts:

Production adoption — 50%. How widely the model is actually used in production: provider traffic, LiteLLM and gateway usage, open-weight downloads and its LMArena rank. Proven, widely used models score highest.
Category benchmark — 30%. The public benchmark that matters for that category — the Artificial Analysis Intelligence Index and LMArena Elo for reasoning, LMArena coding Elo for coding, image and document support plus vision Elo for vision, tool and JSON support for function calling, measured speed for voice.
Tooling & value — 20%. Context window, support for tools, structured outputs and streaming, and price compared with the category median.

Scores of 4.0 and above are shown in green, 3.0–3.9 in yellow, below 3.0 in red. When a model has no public data for a part, it gets a neutral value instead of an invented number.

A “Verified benchmark” mark (✓) means an independent lab — LMArena or Artificial Analysis — has actually tested that model, and the source figure is shown. Models without that mark are labelled “Estimated from published specs”: the score comes from the published context window, tool and schema support, price and adoption only, because no independent lab has tested them yet.

One exception: a brand-new top-tier model that scores very high on the independent benchmarks but has no usage history yet is rated on the benchmark (60%) and tooling & value (40%) alone, so a missing adoption figure can't hold it back. All inputs come from public sources and are refreshed weekly.

Adoption and benchmark inputs come from public sources — LMArena, Artificial Analysis Intelligence Index and published model specs. Updated weekly.

COMMUNITY

What developers say about OpenAI models

LMArena—Speed—Sources: LMArena, Artificial Analysis

Summarised from publicly published developer and community reviews of this model family. Opinions are the sources’, not LLM.API’s, and may not be specific to Azure OpenAI Text Embedding 3 Small.

Long-horizon coding reports on GPT-6 Astra describe a clear step up over the GPT-5.x line, with the higher reasoning tiers seen as the sweet spot for planning and implementation in large (100K+ LOC) codebases.
The same field reports note that fast/ultra modes burn quota quickly and that very large refactors still stall, so teams tend to mix a cheap tier for routine calls with a reasoning tier for hard steps.
Community threads temper the hype: capability gains are acknowledged, but developers still report the usual failure modes on obscure reverse-engineering and modding work.

SOURCES

Frequently Asked Questions

How is Azure OpenAI Text Embedding 3 Small priced on LLM.API?
Listed pricing metadata shows: In $0.02 / 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 Small a chat model?
No—Azure OpenAI Text Embedding 3 Small is categorized as a embedding model. Use the matching API surface rather than assuming chat completions.
What is the context length for Azure OpenAI Text Embedding 3 Small?
Reported context for Azure OpenAI Text Embedding 3 Small is 8K tokens. Always verify the active provider row if multiple providers are listed.
What modalities does Azure OpenAI Text Embedding 3 Small support?
Azure OpenAI Text Embedding 3 Small accepts text and produces embedding according to its architecture metadata on LLM.API.
Does Azure OpenAI Text Embedding 3 Small support streaming?
Streaming depends on the active provider; check the providers table on this page for flags.
What is Azure OpenAI Text Embedding 3 Small?
Azure-hosted OpenAI text-embedding-3-small. Provisional: requires the Azure deployment to be named exactly `text-embedding-3-small` until ProviderKeyOptions.AzureDeploymentType plumbing lands for embeddings. 1536-dim default, Matryoshka shrinkable. On LLM.API it is addressed as `azure/text-embedding-3-small`.
Where is the canonical page for Azure OpenAI Text Embedding 3 Small?
https://llmapi.ai/models/azure-text-embedding-3-small/
What are limitations of Azure OpenAI Text Embedding 3 Small?
Like other API models, Azure OpenAI Text Embedding 3 Small can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.

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Azure OpenAI Text Embedding 3 Small API: Endpoint & Pricing

What is Azure OpenAI Text Embedding 3 Small?

Can you self-host Azure OpenAI Text Embedding 3 Small?

Providers

Try this model

Supported endpoints & features

Errors & fallback routing

Azure OpenAI Text Embedding 3 Small in production

Cross-document matching

Low-latency lookups

Semantic vectorization

Batch throughput

Corpus analytics

Unified AI Routing

Cost Control

Reliability Layer

Observability

Drop-in SDKs

Model Breadth