mteb
Massive Text Embedding Benchmark
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later (supports up to 3.14).
- torch and transformers must be installed; GPU support depends on torch configuration.
- Low friction installation with a pure-Python wheel.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows use in commercial and proprietary projects with minimal restrictions.
last release 2026-08-14 (0 days) · last repo commit 2026-08-14 · 3,393 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 3,180,141 downloads/mo, #2,710 on PyPI
Alternatives
Verify before relying
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)- 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.
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 on this page. Verify before relying on it.
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
mteb on PyPI
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.
Requires Python 3.10 or later (supports up to 3.14). torch and transformers must be installed; GPU support depends on torch configuration.
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)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 the current Python release <3.15,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 14 packagesdatasetsnumpyrequestsscikit-learnscipysentence_transformerstransformerstyping-extensionstorchtqdmrichpytrec-eval-terrierpydanticpolars |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 3,180,141 / month, #2,710 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleIntended Audience :: DevelopersIntended Audience :: Information TechnologyOperating System :: OS IndependentProgramming Language :: Python |
Evidence: mteb-2.19.1-py3-none-any.whl
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