MTEB Leaderboard
Massive Text Embedding Benchmark leaderboard covering 58 datasets across 112 languages and 8 embedding tasks. Industry-standard benchmark for comparing text embedding models.
About this tool
Overview
MTEB (Massive Text Embedding Benchmark) is a massive benchmark for measuring the performance of text embedding models on diverse embedding tasks. MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages.
Expansion: MMTEB
MMTEB (Massive Multilingual Text Embedding Benchmark) expands MTEB to over 500 quality-controlled evaluation tasks across 1,000+ languages, making it the most comprehensive multilingual embedding benchmark.
Current Top Models (2025-2026)
gte-Qwen3-8B
- Largest of a family of new embedding models built on top of Qwen3
- Outperforms previous generation Qwen embedding models
- Ranks high on both multi-lingual and English-only MTEB leaderboards
NV-Embed-v2 (NVIDIA)
- Released October 2025
- Fine-tuned from Llama-3.1-8B
- Particularly powerful at understanding multilingual text
- Previous version (NV-Embed) achieved score of 69.32 on MTEB (56 embedding tasks)
Benchmark Tasks
MTEB covers 8 main embedding tasks:
- Classification
- Clustering
- Pair classification
- Reranking
- Retrieval
- Semantic Textual Similarity (STS)
- Summarization
- Bitext mining
How to Access
Official MTEB leaderboard available at: https://huggingface.co/spaces/mteb/leaderboard
Users can select different benchmarks:
- Multilingual leaderboard
- English-only leaderboard
- Domain-specific benchmarks
Research Impact
MTEB has become the de facto standard for:
- Comparing embedding model performance
- Selecting appropriate models for specific tasks
- Tracking progress in text embedding research
- Validating new embedding approaches
Loading more......
Information
Categories
Tags
Similar Products
6 result(s)