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Provider Input Output License

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zembed-1-embedding

zeroentropy

However, SOTA embedding models are closed-source and proprietary. At ZeroEntropy, we've trained a SOTA 4B open-weight multilingual embedding model that outperforms every competitor we benchmarked, and we're launching it here on HuggingFace. This model outperforms `OpenAI text-embedding-large`, `Cohere Embed v4`, `gemini-embedding-001`, and `voyage-4-nano` across finance, healthcare, legal, conversational, manufacturing, code, and STEM. zembed-1 is distilled directly from our SOTA reranker zerank-2 using our zELO methodology, which models relevance scores as adjusted Elo ratings. Standard contrastive training on binary labels can't match this signal. See our blog post for details.

Embeddings & similarity

Input Text→ Output Embedding

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zerank-1-reranker

zeroentropy

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Basic profile indexed · More details to follow

Category pending

Input Not verified→ Output Not verified

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zerank-1-small-reranker

zeroentropy

This 1.7B reranker is the smaller version of our flagship model zeroentropy/zerank-1. Though the model is over 2x smaller, it maintains nearly the same standard of performance, continuing to outperform other popular rerankers, and displaying massive accuracy gains over traditional vector search. We release this model under the open-source Apache License 2.0, in order to support the open-source community and push the frontier of what's possible with open-source models. NDCG@10 scores between `zerank-1-small` and competing closed-source proprietary rerankers. Since we are evaluating rerankers, OpenAI's `text-embedding-3-small` is used as an initial retriever for the Top 100 candidate documents.

Category pending

Input Not verified→ Output Not verified

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zerank-2-reranker

zeroentropy

In search engines, rerankers are crucial for improving the accuracy of your retrieval system. However, SOTA rerankers are closed-source and proprietary. At ZeroEntropy, we've trained a SOTA reranker outperforming closed-source competitors, and we're launching our model here on HuggingFace. This reranker outperforms proprietary rerankers such as `cohere-rerank-v3.5` and `gemini-2.5-flash` across a wide variety of domains, including finance, legal, code, STEM, medical, and conversational data.

Category pending

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COST EXPLORER

Great models. Clear costs.

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.

MODEL HANDBOOK

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Selection guide & FAQ

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 4

zembed-1-embedding

zerank-1-reranker

zerank-1-small-reranker

zerank-2-reranker

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