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bge-micro

TaylorAI

This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. It is distilled from bge-small-en-v1.5, with 1/4 the non-embedding parameters. It has 1/2 the parameters of the smallest commonly-used embedding model, all-MiniLM-L6-v2, with similar performance. Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: https://seb.sbert.net

Embeddings & similarity

Input Text→ Output Embedding

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bge-micro-v2

TaylorAI

This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. Distilled in a 2-step training process (bge-micro was step 1) from `BAAI/bge-small-en-v1.5`. Using this model becomes easy when you have sentence-transformers installed:

Embeddings & similarity

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gte-tiny

TaylorAI

This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. It is distilled from `thenlper/gte-small`, with comparable (slightly worse) performance at around half the size. Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: https://seb.sbert.net

Embeddings & similarity

Input Text→ Output Embedding

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Cost analysis

Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
Compares base text-token rates within the same channel

Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.

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How should API prices be compared?+

Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

Do open weights mean free commercial use?+

Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

Is the top-ranked model the best for me?+

Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.

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Explore AI models 3

bge-micro

bge-micro-v2

gte-tiny

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