Remova Logo
Quick Profile

OpenAI: Text Embedding 3 Small

OpenAI: Text Embedding 3 Small is a cost-efficient model with standard context support, suited to semantic retrieval and enterprise search for enterprise teams.

Try OpenAI: Text Embedding 3 Small with your team

Last reviewed: 2026-06-09

OpenAI: Text Embedding 3 Small

OpenAI

Stable
Context Window
8,192
Input / 1M
$0.03
Output / 1M
$0.00

What can you do with OpenAI: Text Embedding 3 Small?

Practical ways teams can use OpenAI: Text Embedding 3 Small inside governed AI workflows.

01

Improve enterprise search with OpenAI: Text Embedding 3 Small

Rank documents, answers, and knowledge-base results so teams find the right information faster with OpenAI: Text Embedding 3 Small.

02

Power semantic retrieval with OpenAI: Text Embedding 3 Small

Match user questions to relevant policies, product docs, tickets, and internal references with OpenAI: Text Embedding 3 Small.

03

Deduplicate knowledge assets with OpenAI: Text Embedding 3 Small

Cluster related content, similar records, and overlapping documents for cleaner operations with OpenAI: Text Embedding 3 Small.

04

Route support requests with OpenAI: Text Embedding 3 Small

Classify incoming questions and connect them with the most relevant internal resources with OpenAI: Text Embedding 3 Small.

05

Rank compliance evidence with OpenAI: Text Embedding 3 Small

Surface the most relevant policies, logs, and documents during audits and reviews with OpenAI: Text Embedding 3 Small.

06

Measure content similarity with OpenAI: Text Embedding 3 Small

Compare records, tickets, snippets, and documents for matching or recommendation workflows with OpenAI: Text Embedding 3 Small.

Why this model

OpenAI: Text Embedding 3 Small is available in Remova as a standard context option with $0.03 per 1M tokens input pricing, $0.00 per 1M tokens output pricing, and text->embeddings modality support for enterprise AI operations.

  • OpenAI: Text Embedding 3 Small offers standard context capacity for enterprise prompts and documents.
  • Current Remova pricing band is cost-efficient: $0.03 per 1M tokens input and $0.00 per 1M tokens output.
  • Best-fit workloads include: Semantic retrieval, Enterprise search, Knowledge indexing.
  • Keep role-based access in place before broad rollout.

At a glance

Model ID
openai/text-embedding-3-small
Context Window
8,192 tokens
Modality
text->embeddings
Input Modalities
text
Output Modalities
embeddings
Input Price
$0.03 per 1M tokens
Output Price
$0.00 per 1M tokens
Provider
OpenAI
Listing Date
2025-10-30

Strengths

  • OpenAI: Text Embedding 3 Small is suited for semantic retrieval.
  • Supports standard context for multi-step prompts and larger working sets.
  • Pricing profile is cost-efficient, enabling predictable workload routing decisions.
  • Can be paired with policy guardrails for safer deployment at scale.

Tradeoffs

  • Without workload routing, teams may overuse this model for requests that fit lower-cost tiers.
  • Standard context limits may require chunking or retrieval strategies for large documents.
  • Low-cost tiers can still underperform on high-consequence decisions without escalation paths.
  • Embedding and retrieval systems need benchmark sets to catch ranking drift and stale indexes.

Best for

  • OpenAI: Text Embedding 3 Small for semantic retrieval, ranking, and enterprise search workflows.
  • OpenAI: Text Embedding 3 Small for enterprise search across policies, product docs, and support knowledge bases.
  • OpenAI: Text Embedding 3 Small for indexing internal knowledge assets into searchable vector workflows.
  • OpenAI: Text Embedding 3 Small for surfacing compliance evidence and related records during audits.

Rollout checklist

  • Define where OpenAI: Text Embedding 3 Small is default vs. fallback in your routing policy.
  • Enable role-based access and policy checks before opening access broadly.
  • Set spend guardrails by team and monitor weekly token consumption.
  • Start with one workflow, then expand after you verify quality and spend.
  • Re-run quality and cost benchmarks monthly as newer releases appear.

Related models

Explore adjacent model profiles for routing and benchmarking decisions.

Free Resource

Where Should Your Team Start with AI?

Tell us your industry and team size. We'll tell you which AI use cases will save the most time with the least setup.

You get

A shortlist of AI use cases ranked by impact and effort for your situation.

Tuning notes

frequency_penalty

Tune repetition control for long responses in multi-step workflows.

logit_bias

Use this parameter only with tested defaults in production workflows.

logprobs

Use this parameter only with tested defaults in production workflows.

max_completion_tokens

Use this parameter only with tested defaults in production workflows.

Free Assessment

What Could Go Wrong?

5 questions about how your company uses AI today. We'll show you the risks most companies miss until it's too late.

You get

A risk breakdown with the 3 things you should fix first.

Book demo
Knowledge Hub

OpenAI: Text Embedding 3 Small FAQs

Choose OpenAI: Text Embedding 3 Small when the workload aligns with semantic retrieval, enterprise search, knowledge indexing and quality targets justify its pricing profile.
It depends on workload mix. Most organizations use routing policies so routine traffic stays on lower-cost tiers.
Validate quality on real internal prompts, token efficiency, latency, and policy compliance behavior.

Deploy This Model With Governance

Use policy controls, role-based access, and budget guardrails before enabling advanced model tiers at scale.

Try OpenAI: Text Embedding 3 Small with your team