Codesota · Natural Language Processing · Feature Extraction · MTEB LeaderboardTasks/Natural Language Processing/Feature Extraction
Feature Extraction · benchmark dataset · 2022 · EN

MTEB Leaderboard.

Massive Text Embedding Benchmark across 8 task categories

Submit a result ↵
§ 01 · Leaderboard

Best published scores.

6 results indexed across 1 metric. Shaded row marks current SOTA; ties broken by submission date.

Primary
accuracy · higher is better
avg-score
6 rows
#ModelOrgSubmittedPaper / codeavg-score
01NV-Embed-v2OSSNVIDIASep 2024NV-Embed: Improved Techniques for Training LLMs as Gener…72.31
02GTE-Qwen2-7B-instructOSSAlibabaJun 2024arxiv72.05
03voyage-3-largeVoyage AIJan 2025arxiv70.32
04E5-Mistral-7B-instructOSSMicrosoftJan 2024Improving Text Embeddings with Large Language Models66.63
05jina-embeddings-v3OSSJina AISep 2024jina-embeddings-v3: Multilingual Embeddings With Task Lo…65.18
06text-embedding-3-largeOpenAIJan 2024arxiv64.60
Fig 2 · Rows sorted by score within each metric. Shaded row marks SOTA. Dates reflect model or paper release where available, otherwise the date Codesota accessed the source.
§ 04 · Literature

3 papers
tied to this benchmark.

Every paper below corresponds to at least one row in the leaderboard above. Click through for the arXiv preprint and, when available, the reference implementation.

§ 06 · Contribute

Have a score that beats
this table?

Submit a checkpoint and a reproduction script. We will run it, publish the score, and — if it takes the top — annotate the step on the progress chart with your name.

Submit a result ↵Read submission guide
What a submission needs
  • 01A public checkpoint or API endpoint
  • 02A reproduction script with frozen commit + seed
  • 03Declared evaluation environment (Python, deps)
  • 04One row per metric declared by this dataset
  • 05A contact so we can follow up on discrepancies