---
id: mteb
version: "2.19.1"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# mteb — Massive Text Embedding Benchmark

License: permissive · Maintenance: active · Downloads: 3.2M/mo

## What it is and what it does

MTEB is a benchmarking framework for evaluating text and multimodal embeddings against a collection of standardized tasks. It provides both a Python API and a command-line interface to run models through tasks like text classification, semantic similarity, and information retrieval, collecting results that feed into a public leaderboard. The package wraps embedding models (from sentence_transformers, transformers, or custom implementations) and executes them against predefined task datasets, scoring performance using domain-specific metrics.

The framework is designed for researchers and practitioners who need to compare embedding quality across models or track performance improvements. It handles model loading, task selection, evaluation execution, and result aggregation. Dependencies include datasets for task data, numpy and scipy for numerical computation, torch and transformers for deep learning, and scikit-learn for metrics—a typical modern ML stack. The package is actively maintained and in production use.

Use it for:

- Benchmark a new sentence transformer model against standard tasks to compare its quality to published results.
- Run MTEB tasks via CLI to evaluate multiple embedding models and generate results for submission to the leaderboard.
- Select specific task subsets (e.g., only classification or retrieval) to evaluate embeddings on domain-relevant problems.
- Load and analyze existing model results from the benchmark to understand performance patterns across tasks.
- Integrate MTEB evaluation into a model training pipeline to measure embedding quality during development.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

MTEB evaluates text and multimodal embeddings against a suite of standardized tasks and benchmarks, providing scores and rankings for embedding models and retrieval systems.

Yes. MTEB is actively maintained, has no known vulnerabilities, uses a permissive license, and offers low-friction installation. It is the standard tool for evaluating text embeddings and retrieval systems in the community. Install it if you need to benchmark embeddings, compare models, or contribute results to the public leaderboard.

## Install

pip install mteb
uv add mteb
poetry add mteb

## Installing mteb

Before you install: Low friction installation with a pure-Python wheel. Active maintenance with recent release. Depends on 14 runtime packages including torch, transformers, and sentence_transformers—a substantial but standard ML stack.

License in practice: Apache-2.0 permissive license allows use in commercial and proprietary projects with minimal restrictions.

Quickstart:

pip install mteb

import mteb
from sentence_transformers import SentenceTransformer

model = mteb.get_model("sentence-transformers/all-MiniLM-L6-v2")
tasks = mteb.get_tasks(tasks=["Banking77Classification.v2"])
results = mteb.evaluate(model, tasks=tasks)

Requires Python 3.10 or later (supports up to 3.14). torch and transformers must be installed; GPU support depends on torch configuration.

Verify before relying:

- Whether all 14 runtime dependencies are required for basic evaluation or if subsets can be used for specific tasks.
- Whether GPU support is automatic or requires separate configuration beyond torch installation.
- Performance characteristics and typical runtime for evaluating a single model across the full benchmark suite.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 3.2M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

embedding evaluation benchmark, text embedding quality assessment, retrieval system benchmarking, embedding model comparison, multimodal embedding evaluation, information retrieval benchmark, embedding leaderboard, embedding-evaluation, benchmark-suite, information-retrieval

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