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Embeddings Cost Calculator

Calculate embedding costs for your document collection. Compare pricing across OpenAI, Cohere, Voyage, and Google embedding models with chunking estimates.

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Select models to compare

Chunks / Doc

6

Total Chunks

6,000

Total Tokens

3,000,000

ModelProvider$/1M TokensDimensionsCost/DocTotal Cost
Doubao Embedding
VolcengineFree2,560$0.00$0.00
Doubao Embedding Large
VolcengineFree2,048$0.00$0.00
Doubao Embedding Large Text 240915
VolcengineFree4,096$0.00$0.00
Doubao Embedding Large Text 250515
VolcengineFree2,048$0.00$0.00

Cost Comparison

Doubao Embedding
$0.00
Doubao Embedding Large
$0.00
Doubao Embedding Large Text 240915
$0.00
Doubao Embedding Large Text 250515
$0.00

Token estimation assumes ~1.33 tokens per word or ~4 characters per token. Chunks may exceed model max input length for some providers; real implementations truncate accordingly. Google Gemini Embedding is free within usage limits. Prices reflect current published rates.

How to Use Embeddings Cost Calculator

  1. 1

    Enter document details

    Input the number of documents, average length (in words or characters), chunk size, and overlap.

  2. 2

    Select models to compare

    Toggle embedding models on or off to compare 3 or more models side by side.

  3. 3

    Review chunking stats

    See how many chunks your documents produce and total tokens to embed.

  4. 4

    Compare costs

    View total embedding cost and cost per document for each model, sorted cheapest first.

Frequently Asked Questions

Chunks per document = ceil(document_tokens / (chunk_size - chunk_overlap)). For example, a 2000-word document (~2660 tokens) with 500-token chunks and 50-token overlap produces ceil(2660/450) = 6 chunks.

Chunk overlap means each chunk shares some tokens with the previous chunk, improving retrieval quality at chunk boundaries. Common values are 10-20% of chunk size. More overlap means more chunks and higher cost.

We estimate 1.33 tokens per word for English text (the average across major tokenizers). For character input, we use 4 characters per token. Actual counts vary by language and content.

Google offers Gemini Embedding at no cost within certain usage limits. For high-volume production use, check Google's current terms as free tier limits may apply.

For most use cases, OpenAI text-embedding-3-small offers the best balance of cost and quality. For higher retrieval accuracy, text-embedding-3-large or Voyage 3 are good choices. Cohere embed-v4 excels at multilingual content.

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