zembed 1 embedding

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released March 202641K context≈ 30,720 words

zembed 1 is a text embedding model that converts input text into dense vector representations, supporting a generous 40,960-token context window — well above many embedding models in its class. It operates on text-only inputs and produces embeddings rather than generated text, making it a focused, single-purpose tool. The CC-BY-NC-4.0 license means it's freely usable for non-commercial work, with open weights available for local deployment.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Long Context

Strong

Factual Knowledge

zembed 1 embedding

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released March 202641K context≈ 30,720 words

zembed 1 is a text embedding model that converts input text into dense vector representations, supporting a generous 40,960-token context window — well above many embedding models in its class. It operates on text-only inputs and produces embeddings rather than generated text, making it a focused, single-purpose tool. The CC-BY-NC-4.0 license means it's freely usable for non-commercial work, with open weights available for local deployment.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Long Context

Strong

Factual Knowledge

Glossary

CC-BY-NC-4.0 LicenseA Creative Commons license that allows free use and modification of the model for non-commercial purposes only, with attribution required.Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.Dense VectorA compact numerical representation where most values are non-zero, used to efficiently store and compare the meaning of text.EmbeddingA dense numerical vector that represents a word, sentence, or concept in a high-dimensional space.Embedding ModelA model that converts text into numerical vectors that capture semantic meaning, allowing computers to understand and compare the similarity between different pieces of text.EmbeddingsNumerical representations of text that capture semantic meaning, allowing the model to measure similarity between different words or phrases.Local DeploymentRunning a model directly on your own computer or server instead of sending requests to a remote service.Text EmbeddingA technique that converts text into numerical vectors that capture semantic meaning, allowing the model to understand and compare text similarity.Text Embedding ModelA neural network that converts text into numerical vectors that capture semantic meaning, allowing computers to understand and compare text similarity.TokenA small unit of text (a word, subword, or punctuation mark) that a language model breaks input into for processing.WeightsThe numerical parameters inside a neural network that determine how it processes input and generates output.

Capabilities

Capabilities

Use Case Fit

Glossary