Embedding API Cost 2026: OpenAI vs Cohere vs Jina AI

Embedding APIs power semantic search, RAG pipelines and recommendation engines. At scale, the wrong model can cost 7× more than the cheapest option. Here is the 2026 price breakdown.

How Embedding Pricing Works

All embedding APIs charge per token (input only — there is no output). Costs are quoted per million tokens. A typical document of 500 words ≈ 650 tokens. At 1M documents, that is 650M tokens.

2026 Embedding API Price Table

OpenAI text-embedding-3-small: Best Balance

At $0.020/1M tokens, text-3-small is 5× cheaper than ada-002 and competitive with Jina. It supports Matryoshka Representation Learning — you can truncate to 512 dims without significant quality loss, halving your vector DB storage.

Jina AI v3: Cheapest at Scale

Jina v3 at $0.018/1M tokens undercuts OpenAI by 10%. Supports task-aware encoding (query vs passage) and long documents (8192 tokens). Good for large-scale RAG pipelines where cost dominates.

Cost at Scale: 1B Tokens/Month

FAQ

How much does OpenAI embedding cost?

text-embedding-3-small costs $0.020 per 1M tokens. text-embedding-3-large costs $0.130/1M. Ada-002 (legacy) costs $0.100/1M. For most use cases, text-3-small offers the best cost/quality ratio.

Which embedding model is cheapest?

Jina AI v3 at $0.018/1M tokens is currently the cheapest quality embedding API. OpenAI text-3-small at $0.020/1M is close and benefits from OpenAI ecosystem integrations.

How do I reduce embedding costs?

Use text-3-small instead of text-3-large (6.5× cheaper). Cache embeddings — most documents don't change. Use batch embedding APIs where available. Truncate dimensions to 512 if quality holds.