Compare price per million tokens, dimensions, max context length, and speed across 13 leading embedding models — then estimate your real monthly cost. 100% client-side, nothing leaves your browser.
Monthly Cost Estimator
Enter your expected monthly token volume to see projected costs for every model.
Roughly 750 words ≈ 1,000 tokens. A 10-page PDF ≈ 5,000 tokens.
Avg. 500 tokens/doc assumed. Leave blank to use tokens above directly.
Model Comparison
Click any column header to sort. Bars show relative cost per million tokens (shorter is cheaper).
Model↕
Provider↕
$ / 1M tokens↕
Dimensions↕
Max Tokens↕
Speed↕
Est. Monthly Cost↕
No models match the current filters. Try widening your criteria.
Building an AI product and want early access to more free ABUZ8 tools and templates?
Strong accuracy at 3072 dimensions with Matryoshka truncation support. Great default for production RAG pipelines that need high recall.
Code Search
voyage-code-3
Purpose-built for source code semantics — outperforms general-purpose models on function and snippet retrieval tasks.
Multilingual Content
embed-multilingual-v3.0
Cohere's multilingual model covers 100+ languages with consistent quality, ideal for global search and support content.
Budget / High Volume
BGE-M3 (self-hosted)
Free to run locally with competitive multilingual quality. Best when you already have GPU/CPU capacity and want to avoid per-token fees.
Frequently Asked Questions
What is a vector embedding and why does the model choice matter?
A vector embedding is a numeric representation of text that captures its meaning, allowing computers to measure semantic similarity. The embedding model you choose determines dimensionality, accuracy, max input length, and cost — all of which affect retrieval quality, storage size, and your monthly bill.
Should I use a cloud API or a local open-source embedding model?
Cloud APIs (OpenAI, Voyage AI, Cohere) offer strong accuracy with zero infrastructure management but charge per token. Local models (BGE-M3, all-MiniLM-L6-v2, nomic-embed-text) are free to run but require your own compute (CPU/GPU) and ops overhead. High-volume or privacy-sensitive workloads often favor local models; low-volume or fast-moving projects often favor APIs.
Do higher embedding dimensions always mean better quality?
Not necessarily. Higher dimensions can capture more nuance but increase storage and query latency in your vector database. Many modern models (like text-embedding-3-large) support dimension truncation via Matryoshka representation learning, letting you shrink vectors with minimal quality loss.
How do I estimate embedding costs for a RAG pipeline?
Estimate total tokens by multiplying your document count by average tokens per document, then add query-time embedding tokens for search requests. Multiply total monthly tokens by the model's price per million tokens. Re-embedding on document updates should also be factored into recurring costs.