AI21 Labs

Paid

Enterprise language model APIs

#open-source#benchmarking#large language models#AI21 Labs#GitHub#research#developers#model performance#custom properties#compliance decoration#add-on tasks#toolkit#documentation#collaborative#community

Type

Api

Founded

2017

Company

AI21 Labs

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About AI21 Labs

AI21 Labs is an AI lab & product company with a mission to revolutionize how we communicate. Our mission is to use AI to create a thought partnership between machines and humans, transforming the way we read and write. Our team of AI experts have developed a suite of products and services designed to help people interact with information in new and more meaningful ways. Our solutions are built on the latest AI technologies, providing users with a powerful toolset to facilitate communication, collaboration, and knowledge sharing. Our products are designed to enable users to read and write faster and with greater accuracy, while also providing a platform to share and collaborate with others. AI21 Labs’ products and services can be used by individuals, teams, and organizations, helping to reduce reliance on manual processes and freeing up time for more important tasks. With AI21 Labs, you can take advantage of the power of AI to help you communicate more effectively and efficiently.

Key Features

Large Language Model (LLM) evaluation toolkit for benchmarking capabilities and limitations

Open-source GitHub repository encouraging transparency and collaboration

Mission-aligned with making machines thought partners to humans

Support for custom properties and compliance decoration of projects

Extensible evaluation framework for adding tasks and custom metrics

Documentation and property settings to guide usage and adaptation

Enterprise- and community-oriented development and contributions

Official SDKs for Python and TypeScript to simplify integration

Ability to test and compare multiple models at scale

Focus on factuality, contextual retrieval, and tokenization across public projects

Best For

Research scientists: Benchmark novel LLMs against established baselines across diverse tasks to quantify improvements.ML engineers: Compare multiple proprietary and open-source models to select the best model for a production use case.Data scientists: Evaluate fine-tuned models to verify performance gains on domain-specific datasets.Product managers: Validate model choices with objective metrics before committing to roadmap and budget.Academic researchers: Reproduce evaluation results and publish standardized benchmarks for peer review.Compliance and governance teams: Annotate evaluations with custom properties to track data sensitivity and compliance frameworks.Open-source contributors: Extend the framework with new tasks, metrics, and adapters for broader coverage.Startups: Assess cost-performance tradeoffs across models to optimize for latency, quality, and budget.Educators: Teach NLP evaluation methodologies using an accessible, well-documented open-source suite.Enterprise AI teams: Integrate consistent evaluation pipelines across business units with governance-ready settings.

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