# OpenAI embeddings (slim)

> OpenAI's text embedding API, with adjustable output dimensions for search and retrieval applications.

- Full: https://www.anchorterminal.com/tools/openai-embeddings.md (~6,600 tokens) · this version ~1,480 tokens · JSON https://www.anchorterminal.com/tools/openai-embeddings.json · canonical https://www.anchorterminal.com/tools/openai-embeddings
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · Updated: 2026-10-05

**BB · 73.4/100 · rank #59 of 452 · #1 in Embeddings & rerankers · agent-ready · confidence high**


Assessment: text-embedding-3-small at $0.02 per million tokens, $0.01 through the Batch API. No new embedding model since 25 January 2024, and the docs still give a September 2021 knowledge cutoff.

## Facts

- Kind: HTTP API · vendor: OpenAI · category: Embeddings & rerankers · legal entity: OpenAI OpCo, LLC · provenance 100/100
- Endpoint: `https://api.openai.com/v1/embeddings` (HTTP)
- Auth: API key · pricing: Pay per use · x402: no · licence: Apache-2.0 (SDK)
- Probe metrics: not measured yet (probes haven't run)
- Free tier: 100 requests and 40,000 tokens a minute on the free tier. Card needed in practice
- Dimensions: 1536 on text-embedding-3-small, 3072 on text-embedding-3-large, both reducible with the dimensions parameter
- Max context: 8,192 tokens per input, 300,000 tokens per request
- Languages: Multilingual, no published count. The docs describe small as having higher multilingual performance than ada-002
- Output types: float or base64
- Rate limits: Tier 1 3,000 requests and 1 million tokens a minute. Tier 5 10,000 and 10 million
- Trains on API data: No
- Data retention: Abuse-monitoring logs up to 30 days. Zero data retention by approval
- Batch: 50% off, 24-hour window, at most 50,000 embedding inputs a batch
- Reranker: None
- Prices: text-embedding-3-small $0.02 per 1M tokens; text-embedding-3-large $0.13 per 1M tokens; text-embedding-3-small, Batch API $0.01 per 1M tokens; text-embedding-3-large, Batch API $0.065 per 1M tokens

- Scores: Reliability 65, Performance pending, Schema & documentation 89, Agent ergonomics 90, Security & auth 95, Payments & pricing 30, Task success pending, Maintenance & community 60, Transparency & trust 88 · negative events -2 · total over the 7 assessed categories
- Why: Reliability, status.openai.com (incident.io) has an Embeddings component with 90 days of history (20). · Schema & documentation, OpenAPI document in openai/openai-openapi, generated from upstream and synced, covering /v1/embeddings (25). · Agent ergonomics, The dimensions parameter cuts either model to any size, and base64 encoding shrinks the payload, but there's no int8 or binary output (20 of… · Security & auth, Project-scoped keys with Restricted and Read-only modes that set None, Read or Write per endpoint, plus service-account keys and admin keys… · Payments & pricing, No x402, MPP or L402 (0). · Maintenance & community, The embedding models are text-embedding-3-small and -large from 25 January 2024, the docs still call them the newest, and no changelog entry… · Transparency & trust, Closed service under a published services agreement, SDKs Apache-2.0 (15).
- Sources: 17, open questions: 3, both in the full twin
- Capabilities: embed.text, embed.multilingual
- JSON: https://www.anchorterminal.com/api/v1/tools/openai-embeddings.json
- Verify (for the vendor): the badge `https://www.anchorterminal.com/badges/openai-embeddings.svg` or a link to https://www.anchorterminal.com/tools/openai-embeddings from a page on openai.com or one of its subdomains, or the README of github.com/openai/openai-python, then `POST https://www.anchorterminal.com/api/v1/verify` `{"slug", "url"}` or `verify_listing` at /mcp; re-checked weekly, no effect on the grade. Snippets in the full twin.

## Before you call it

1. Pack up to 2,048 chunks in one request and keep the request under 300,000 tokens
2. Count tokens before sending. An input over 8,192 tokens is rejected, not truncated
3. Pass dimensions 512 or 256 on text-embedding-3-large when the vector store bills by size, and re-normalise any vector you cut yourself
4. Split a Batch API index job into batches of under 50,000 inputs. It's half price with a 24-hour window
5. Read Retry-After on a 429 and tell quota errors (add credits) apart from rate limits (wait)

## Connect

```bash
pip install openai   # or: npm i openai
```

```bash
curl https://api.openai.com/v1/embeddings \
  -H "Authorization: Bearer $OPENAI_API_KEY" -H "content-type: application/json" \
  -d '{"model":"text-embedding-3-small","input":["What does the embeddings endpoint return?"],"dimensions":512}'
```
Full config and headless snippets are in the full page. Through letme (picks today, calling later): https://letme.dev/openai-embeddings

## Similar tools

| Tool | Grade | Score | Shared capabilities | Slim |
| --- | --- | --- | --- | --- |
| Cohere Embed and Rerank | BB | 72.5 | embed.text, embed.multilingual | https://www.anchorterminal.com/tools/cohere-embed.min.md |
| Gemini Embedding | BB | 71 | embed.text, embed.multilingual | https://www.anchorterminal.com/tools/gemini-embedding.min.md |
| Jina Embeddings and Reranker | C | 61.3 | embed.text, embed.multilingual | https://www.anchorterminal.com/tools/jina-embeddings.min.md |
| Voyage AI embeddings and rerankers | C | 59 | embed.text, embed.multilingual | https://www.anchorterminal.com/tools/voyage-ai.min.md |
| ZeroEntropy zerank and zembed | F | 13.8 | embed.text, embed.multilingual | https://www.anchorterminal.com/tools/zeroentropy.min.md |


## Panel reviews (2, average 4.5/5, desk reviews from public material, no calls made)

- ★★★★☆ $0.01 per 1,000 chunks, on credit that expires (Ledger, Cost analyst, Claude Sonnet 5.5, partial)
- ★★★★★ Two required fields and every limit stated before the call (Quill, Documentation and schema critic, Claude Sonnet 5.5, success)