Text-Embedding-3-Small: Complete Guide to OpenAI's Most Popular Embedding Model#

text-embedding-3-small is OpenAI's cost-effective embedding model, released in January 2024. It converts text into 1536-dimensional vectors that capture semantic meaning — the foundation for semantic search, RAG pipelines, recommendation systems, and classification tasks.

This guide covers everything: pricing, token limits, dimensions, API usage, dimension reduction, performance benchmarks, and how it compares to text-embedding-3-large.

Text-Embedding-3-Small Quick Reference#

Spec	Value
Model name	text-embedding-3-small
Provider	OpenAI
Default dimensions	1536
Adjustable dimensions	256 – 1536
Max input tokens	8,191
Max batch size	2,048 inputs per request
Pricing (OpenAI direct)	$0.020 per 1M tokens
Pricing (Crazyrouter)	$0.016 per 1M tokens
MTEB benchmark score	62.3
Multilingual	Yes
Output format	float or base64
Release date	January 25, 2024
Status	Active (not deprecated)

Text-Embedding-3-Small Pricing#

text-embedding-3-small costs 0.020per1milliontokens∗∗onOpenAIdirectly.Through[Crazyrouter](https://crazyrouter.com),thepricedropsto∗∗0.020 per 1 million tokens** on OpenAI directly. Through [Crazyrouter](https://crazyrouter.com), the price drops to **0.016 per 1M tokens — a 20% discount.

To put that in perspective:

Document Volume	Approx. Tokens	Cost (OpenAI)	Cost (Crazyrouter)
100 pages of text	~75,000	$0.0015	$0.0012
10,000 pages	~7.5M	$0.15	$0.12
1 million pages	~750M	$15.00	$12.00
Wikipedia (English, full)	~4.4B	$88.00	$70.40

Cost Comparison with Other Embedding Models#

Model	Price / 1M Tokens	Price via Crazyrouter	Dimensions
text-embedding-3-small	$0.020	$0.016	1536
text-embedding-3-large	$0.130	$0.100	3072
text-embedding-ada-002	$0.100	—	1536
Google text-embedding-005	$0.00625	$0.005	768
Cohere embed-v4	$0.100	—	1024
Voyage voyage-3-large	$0.180	—	2048

text-embedding-3-small is 6.5x cheaper than text-embedding-3-large and 5x cheaper than the older ada-002 — while outperforming ada-002 on benchmarks.

For a deeper comparison of all embedding models, see our AI Embeddings Comparison 2026 Guide.

Text-Embedding-3-Small Dimensions#

The default output is a 1536-dimensional vector. But text-embedding-3-small supports dimension reduction via the dimensions parameter — you can request any value from 256 to 1536.

This is done using Matryoshka Representation Learning (MRL). The model is trained so that the first N dimensions of the vector carry the most important information. Truncating to fewer dimensions loses some nuance but keeps most of the semantic signal.

Dimension vs. Quality Tradeoff#

Dimensions	MTEB Score	Storage per Vector	Relative Quality
1536 (default)	62.3	6,144 bytes	100%
1024	~61.5	4,096 bytes	~98.7%
768	~60.8	3,072 bytes	~97.6%
512	~59.7	2,048 bytes	~95.8%
256	~57.8	1,024 bytes	~92.8%

When to Reduce Dimensions#

• 256 dimensions: Prototyping, low-resource environments, or when storage is the bottleneck
• 512 dimensions: Good balance for mobile apps or edge deployments
• 768 dimensions: Matches Google's embedding size — useful for migration
• 1536 dimensions: Production workloads where quality matters most

Text-Embedding-3-Small Token Limit and Context Length#

text-embedding-3-small accepts up to 8,191 tokens per input string. This is the model's context window for embedding.

Key details:

• Tokenizer: cl100k_base (same as GPT-4)
• 1 token ≈ 4 characters in English, ≈ 0.75 words
• 8,191 tokens ≈ 6,100 words ≈ 24,000 characters
• Text exceeding 8,191 tokens is truncated (not rejected)

How to Count Tokens Before Sending#

Handling Long Documents#

For documents longer than 8,191 tokens, you have two options:

1. Chunking: Split into overlapping segments and embed each one
2. Summarize first: Use an LLM to summarize, then embed the summary

How to Use the Text-Embedding-3-Small API#

Python (OpenAI SDK)#

Batch Embedding (Multiple Texts)#

Send up to 2,048 texts in a single request for better throughput:

cURL#

Response Format#

Base64 Output Format#

For bandwidth-sensitive applications, request base64-encoded output:

Error Handling#

Text-Embedding-3-Small vs Text-Embedding-3-Large#

This is the most common comparison. Here's the full breakdown:

Feature	text-embedding-3-small	text-embedding-3-large
Default dimensions	1536	3072
Adjustable dimensions	256 – 1536	256 – 3072
Max tokens	8,191	8,191
MTEB score	62.3	64.6
Price / 1M tokens	$0.020	$0.130
Price via Crazyrouter	$0.016	$0.100
Relative quality	Baseline	+3.7% better
Relative cost	Baseline	6.5x more expensive
Storage (float32)	6 KB/vector	12 KB/vector

When to Choose text-embedding-3-small#

• Budget-conscious projects
• High-volume embedding workloads (millions of documents)
• RAG applications where "good enough" retrieval is fine
• Prototyping and development
• Applications where latency matters (smaller vectors = faster similarity search)

When to Choose text-embedding-3-large#

• Search quality is the top priority and budget allows it
• Legal, medical, or financial domains where precision matters
• Small document collections where the 6.5x cost difference is negligible
• You can use dimension reduction (e.g., 1024 dims) to get large-model quality at reduced storage

The Dimension Reduction Trick#

text-embedding-3-large at 1024 dimensions often outperforms text-embedding-3-small at 1536 dimensions — while using less storage:

Text-Embedding-3-Small vs Ada-002#

text-embedding-ada-002 was OpenAI's previous generation embedding model. Here's why you should migrate:

Feature	text-embedding-3-small	text-embedding-ada-002
MTEB score	62.3	61.0
Price / 1M tokens	$0.020	$0.100
Dimensions	1536 (adjustable)	1536 (fixed)
Dimension reduction	✅ Yes	❌ No
Multilingual (MIRACL)	44.0	31.4

text-embedding-3-small is 5x cheaper, higher quality, and supports dimension reduction. There's no reason to stay on ada-002.

Migration note: Embeddings from different models are not compatible. Switching requires re-embedding all your documents.

Text-Embedding-3-Small Multilingual Support#

text-embedding-3-small supports multilingual text natively. On the MIRACL multilingual benchmark, it scores 44.0 — a massive improvement over ada-002's 31.4.

Supported languages include English, Chinese, Japanese, Korean, Spanish, French, German, Portuguese, Russian, Arabic, Hindi, and many more.

For applications that are primarily multilingual (100+ languages, cross-lingual retrieval at scale), also consider Cohere embed-v4 or the open-source BGE-M3.

Is Text-Embedding-3-Small Deprecated?#

No. As of 2026, text-embedding-3-small is fully active and supported by OpenAI. It is not deprecated and there is no announced deprecation date.

The model that is deprecated is the older text-embedding-ada-002. OpenAI recommends migrating from ada-002 to the text-embedding-3 series.

Timeline:

• text-embedding-ada-002: Released December 2022, now legacy
• text-embedding-3-small: Released January 2024, current recommended model
• text-embedding-3-large: Released January 2024, current premium model

Building Semantic Search with Text-Embedding-3-Small#

Here's a complete working example:

Scaling with a Vector Database#

For production workloads with millions of documents, use a vector database instead of in-memory numpy:

Other popular vector databases that work well with text-embedding-3-small:

• Pinecone: Fully managed, easiest to start
• Weaviate: Open source, supports hybrid search
• Qdrant: Open source, high performance
• ChromaDB: Lightweight, great for prototyping
• pgvector: PostgreSQL extension, no new infrastructure needed

Performance Benchmarks#

MTEB (Massive Text Embedding Benchmark)#

Model	Average	Classification	Clustering	Retrieval
text-embedding-3-small	62.3	67.1	41.2	51.7
text-embedding-3-large	64.6	69.8	44.1	55.4
text-embedding-ada-002	61.0	66.3	40.1	49.3
Cohere embed-v4	66.2	71.5	46.3	56.8
Voyage voyage-3-large	67.1	72.0	47.2	58.1

Multilingual (MIRACL)#

Model	MIRACL Score
text-embedding-3-small	44.0
text-embedding-3-large	54.9
text-embedding-ada-002	31.4

Latency#

text-embedding-3-small is one of the fastest commercial embedding models. Typical latency through Crazyrouter:

Batch Size	Avg Latency
1 text	~50ms
10 texts	~80ms
100 texts	~200ms
1000 texts	~800ms

Best Practices#

1. Batch requests: Send multiple texts per API call (up to 2,048) to reduce overhead
2. Cache embeddings: Never re-embed the same text — store results in a vector database or cache
3. Normalize vectors: The API returns normalized vectors by default, but verify if using dimension reduction
4. Choose dimensions wisely: Start with 1536, reduce only if storage or latency is a real constraint
5. Use consistent models: Never mix embeddings from different models in the same index
6. Chunk long documents: Split texts over 8,191 tokens with overlap for context continuity
7. Monitor token usage: Track usage.total_tokens in responses to manage costs

Frequently Asked Questions#

What is text-embedding-3-small?#

text-embedding-3-small is OpenAI's cost-effective text embedding model. It converts text into 1536-dimensional numerical vectors that capture semantic meaning, enabling applications like semantic search, RAG, classification, and clustering.

How much does text-embedding-3-small cost?#

0.020per1milliontokensonOpenAIdirectly.Through[Crazyrouter](https://crazyrouter.com),itcosts0.020 per 1 million tokens on OpenAI directly. Through [Crazyrouter](https://crazyrouter.com), it costs 0.016 per 1M tokens — 20% cheaper. Embedding 10,000 pages of text costs roughly $0.15.

What is the token limit for text-embedding-3-small?#

8,191 tokens per input string. That's approximately 6,100 words or 24,000 characters in English. Text exceeding this limit is silently truncated.

How many dimensions does text-embedding-3-small output?#

1,536 dimensions by default. You can reduce this to any value between 256 and 1,536 using the dimensions parameter in the API request.

Is text-embedding-3-small deprecated?#

No. It is fully active and supported as of 2026. The deprecated model is the older text-embedding-ada-002.

What's the difference between text-embedding-3-small and text-embedding-3-large?#

text-embedding-3-large outputs 3,072 dimensions (vs 1,536), scores 3.7% higher on MTEB benchmarks, and costs 6.5x more (0.13vs0.13 vs 0.02 per 1M tokens). For most applications, the small model is sufficient.

Does text-embedding-3-small support multilingual text?#

Yes. It handles multiple languages natively and scores 44.0 on the MIRACL multilingual benchmark. No special configuration is needed — just pass text in any supported language.

Can I use text-embedding-3-small with LangChain?#

Yes. LangChain has built-in support:

Can I use text-embedding-3-small with LlamaIndex?#

Yes:

How does text-embedding-3-small compare to free/open-source models?#

Open-source models like BGE-M3 (MTEB 63.2) and E5-Mistral-7B (MTEB 66.6) can match or exceed its quality. The trade-off is you need GPU infrastructure to run them. text-embedding-3-small wins on convenience and total cost for small-to-medium workloads.

Summary#

text-embedding-3-small is the default choice for production embedding workloads in 2026. At 0.02/1Mtokens(or0.02/1M tokens (or 0.016 via Crazyrouter), it delivers strong quality across search, RAG, and classification — with the flexibility of dimension reduction and multilingual support.

Choose text-embedding-3-small when: you want the best price-to-performance ratio for embedding workloads.

Choose text-embedding-3-large when: you need maximum quality and the 6.5x cost increase is acceptable.

Access it through Crazyrouter for 20% lower pricing and a unified API that also gives you GPT-5, Claude, Gemini, and 300+ other models — all with one API key.