{
 "body": "\n## What it measures\n\nmteb_overall is not a task of its own; it is the blended summary the public MTEB leaderboard\nshows by default, combining every task type a given leaderboard variant covers (retrieval,\nclassification, clustering, reranking, STS, summarization, pair classification, and on some\nvariants bitext mining) into one number per model. People call this \"the MTEB score,\" and it\nis what most vendor announcements and comparison charts quote when they cite \"MTEB.\"\n\n## How it is scored\n\nThe Hugging Face leaderboard publishes two blends: a Task Mean, the unweighted average of\nevery individual task's own metric (accuracy, nDCG@10, V-measure, MAP, Spearman correlation\nor average precision, each already on a roughly 0-100 scale), and a Task Type Mean, which\nfirst averages within each task type and then averages those type-level means, so a type with\nmany datasets cannot swamp a type with few. Which blend a given \"overall\" figure represents\ndepends on the leaderboard variant and the column a chart is drawn from; this repository does\nnot assume one over the other unless a source states it. Neither blend is directly comparable\nto any single task's raw metric.\n\n## Dataset and licence\n\nmteb_overall inherits its dataset from whichever MTEB task-set variant is being summarised \u2014\n58 datasets across 112 languages in the original 2022 release, and far more on the 2026 v2\nboards (41 tasks on MTEB(eng, v2); 131 tasks on MTEB(Multilingual, v2)). See the `mteb` family\npage for the full breakdown; there is no dataset unique to the overall figure, and licensing\nvaries per underlying dataset the same way it does for the family.\n\n## Who publishes it\n\nSame authorship and maintenance as the MTEB family: introduced by Niklas Muennighoff,\nNouamane Tazi, Lo\u00efc Magne and Nils Reimers (EACL 2023; preprint October 2022), now maintained\nby the open embeddings-benchmark organisation and the wider MMTEB contributor group, with\nHugging Face hosting the public leaderboard Space that computes and displays the average.\n\n## Lineage\n\nmteb_overall is a subset of, and points back to, the `mteb` family page; its siblings are\n`mteb_retrieval`, `mteb_classification`, `mteb_clustering`, `mteb_reranking`, `mteb_sts`,\n`mteb_summarization` and `mteb_pair_classification`. It has no predecessor or successor of its\nown \u2014 it is a leaderboard column, not an independent benchmark \u2014 so its history tracks\nwhichever version of the underlying task set is current (v1 in 2022; v2 on today's English and\nmultilingual boards).\n\n## Saturation and contamination\n\nBecause it blends many tasks, mteb_overall is structurally harder to saturate than any single\ntask: a model would need to sit near the ceiling on retrieval, clustering, STS and the rest\nsimultaneously to top this column, and none does as of access. The figure carries the same\ncontamination watch as the family: most component datasets are public and some have been\nonline since 2022, so a strong overall score partly reflects how much of that public test data\na model's pretraining mix absorbed, not only raw embedding quality.\n\n## How to run it\n\nReproducing mteb_overall means running every task in the chosen MTEB variant through the\n`mteb` Python package (`pip install mteb`; `mteb run -m <model> -t <benchmark-name>` against a\nnamed benchmark such as `MTEB(eng, v2)`) and letting the package or the leaderboard submission\npipeline compute the mean. There is no separate harness for the aggregate alone, and no\nlm-evaluation-harness, HELM, OpenCompass or BIG-bench task reproduces it.\n\n## Reading the numbers\n\nTreat mteb_overall as a triage signal, not a purchase decision: it says a model is broadly\ncompetent across embedding tasks, but the gap between two models' overall scores can hide one\nbeing much better at retrieval and much worse at clustering, which matters if you only need\none of those. Cross-check the specific task-type subset your application depends on rather\nthan the headline number, and confirm which of Task Mean or Task Type Mean a given figure is\nbefore comparing it to a number pulled from a different chart or vendor announcement.\n",
 "build": {
  "built_at": "2026-09-09T16:56:50+00:00",
  "commit": "0a599558854c0e238c03a0f0d725239cb28f9d11",
  "eligibility_as_of": "2026-09-09"
 },
 "disposition": {
  "canonical_id": "mteb_overall",
  "reasons": [],
  "status": "unassessed",
  "verified_results": []
 },
 "models_covered": [
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge reranker v2 m3",
   "model_id": "baai/bge-reranker-v2-m3",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 70.1,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "NV Embed v2",
   "model_id": "nvidia/nv-embed-v2",
   "provider": "nvidia",
   "provider_display": "NVIDIA",
   "score": 69.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "jina reranker v2 base multilingual",
   "model_id": "jina/jina-reranker-v2-base-multilingual",
   "provider": "jina",
   "provider_display": "Jina AI",
   "score": 68.5,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge m3",
   "model_id": "baai/bge-m3",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 68.2,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Qwen3 Embedding 8B",
   "model_id": "qwen/qwen3-embedding-8b",
   "provider": "qwen",
   "provider_display": "Alibaba / Qwen Team",
   "score": 68.0,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Qwen3 VL Embedding 8B",
   "model_id": "qwen/qwen3-vl-embedding-8b",
   "provider": "qwen",
   "provider_display": "Alibaba / Qwen Team",
   "score": 68.0,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "jina embeddings v4",
   "model_id": "jina/jina-embeddings-v4",
   "provider": "jina",
   "provider_display": "Jina AI",
   "score": 67.8,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "jina embeddings v4 vllm retrieval",
   "model_id": "jina/jina-embeddings-v4-vllm-retrieval",
   "provider": "jina",
   "provider_display": "Jina AI",
   "score": 67.8,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Voyage 3",
   "model_id": "voyage/voyage-3",
   "provider": "voyage",
   "provider_display": "Voyage AI",
   "score": 67.2,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge multilingual gemma2",
   "model_id": "baai/bge-multilingual-gemma2",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 66.8,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "e5 mistral 7B instruct",
   "model_id": "intfloat/e5-mistral-7b-instruct",
   "provider": "intfloat",
   "provider_display": "intfloat",
   "score": 66.8,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "jina embeddings v3",
   "model_id": "jina/jina-embeddings-v3",
   "provider": "jina",
   "provider_display": "Jina AI",
   "score": 66.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Gemini Embedding 001",
   "model_id": "google/gemini-embedding-001",
   "provider": "google",
   "provider_display": "Google DeepMind",
   "score": 66.2,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "nomic embed text v2 moe",
   "model_id": "nomic/nomic-embed-text-v2-moe",
   "provider": "nomic",
   "provider_display": "Nomic AI",
   "score": 65.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "nomic embed text v2 moe GGUF",
   "model_id": "nomic/nomic-embed-text-v2-moe-gguf",
   "provider": "nomic",
   "provider_display": "Nomic AI",
   "score": 65.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Qwen3 Embedding 4B",
   "model_id": "qwen/qwen3-embedding-4b",
   "provider": "qwen",
   "provider_display": "Alibaba / Qwen Team",
   "score": 65.5,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "snowflake arctic embed l v2.0",
   "model_id": "snowflake/snowflake-arctic-embed-l-v2-0",
   "provider": "snowflake",
   "provider_display": "Snowflake",
   "score": 65.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "SFR Embedding 2 R",
   "model_id": "salesforce/sfr-embedding-2-r",
   "provider": "salesforce",
   "provider_display": "Salesforce",
   "score": 65.2,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "text-embedding-3-large",
   "model_id": "openai/text-embedding-3-large",
   "provider": "openai",
   "provider_display": "OpenAI",
   "score": 64.8,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge large zh v1.5",
   "model_id": "baai/bge-large-zh-v1-5",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 64.5,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "multilingual e5 large instruct",
   "model_id": "intfloat/multilingual-e5-large-instruct",
   "provider": "intfloat",
   "provider_display": "intfloat",
   "score": 64.5,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge large en v1.5",
   "model_id": "baai/bge-large-en-v1-5",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 64.2,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge large zh",
   "model_id": "baai/bge-large-zh",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 63.5,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "granite embedding english r2",
   "model_id": "ibm/granite-embedding-english-r2",
   "provider": "ibm",
   "provider_display": "IBM",
   "score": 63.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "granite embedding small english r2",
   "model_id": "ibm/granite-embedding-small-english-r2",
   "provider": "ibm",
   "provider_display": "IBM",
   "score": 63.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Voyage Code 3",
   "model_id": "voyage/voyage-code-3",
   "provider": "voyage",
   "provider_display": "Voyage AI",
   "score": 63.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "snowflake arctic embed l",
   "model_id": "snowflake/snowflake-arctic-embed-l",
   "provider": "snowflake",
   "provider_display": "Snowflake",
   "score": 63.2,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge large en",
   "model_id": "baai/bge-large-en",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 63.0,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Voyage Finance 2",
   "model_id": "voyage/voyage-finance-2",
   "provider": "voyage",
   "provider_display": "Voyage AI",
   "score": 63.0,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "nomic embed text v1.5",
   "model_id": "nomic/nomic-embed-text-v1-5",
   "provider": "nomic",
   "provider_display": "Nomic AI",
   "score": 62.8,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "nomic embed text v1.5 GGUF",
   "model_id": "nomic/nomic-embed-text-v1-5-gguf",
   "provider": "nomic",
   "provider_display": "Nomic AI",
   "score": 62.8,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "snowflake arctic embed m v2.0",
   "model_id": "snowflake/snowflake-arctic-embed-m-v2-0",
   "provider": "snowflake",
   "provider_display": "Snowflake",
   "score": 62.8,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Voyage Multilingual 2",
   "model_id": "voyage/voyage-multilingual-2",
   "provider": "voyage",
   "provider_display": "Voyage AI",
   "score": 62.8,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "e5 large v2",
   "model_id": "intfloat/e5-large-v2",
   "provider": "intfloat",
   "provider_display": "intfloat",
   "score": 62.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Voyage Law 2",
   "model_id": "voyage/voyage-law-2",
   "provider": "voyage",
   "provider_display": "Voyage AI",
   "score": 62.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge base en v1.5",
   "model_id": "baai/bge-base-en-v1-5",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 62.1,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Voyage 3 Lite",
   "model_id": "voyage/voyage-3-lite",
   "provider": "voyage",
   "provider_display": "Voyage AI",
   "score": 62.1,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge base zh v1.5",
   "model_id": "baai/bge-base-zh-v1-5",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 62,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "granite embedding 278M multilingual",
   "model_id": "ibm/granite-embedding-278m-multilingual",
   "provider": "ibm",
   "provider_display": "IBM",
   "score": 61.5,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "nomic embed text v1",
   "model_id": "nomic/nomic-embed-text-v1",
   "provider": "nomic",
   "provider_display": "Nomic AI",
   "score": 61.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "snowflake arctic embed m v1.5",
   "model_id": "snowflake/snowflake-arctic-embed-m-v1-5",
   "provider": "snowflake",
   "provider_display": "Snowflake",
   "score": 61.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "multilingual e5 large",
   "model_id": "intfloat/multilingual-e5-large",
   "provider": "intfloat",
   "provider_display": "intfloat",
   "score": 61.2,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge base zh",
   "model_id": "baai/bge-base-zh",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 61,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "e5 large",
   "model_id": "intfloat/e5-large",
   "provider": "intfloat",
   "provider_display": "intfloat",
   "score": 61.0,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "snowflake arctic embed m long",
   "model_id": "snowflake/snowflake-arctic-embed-m-long",
   "provider": "snowflake",
   "provider_display": "Snowflake",
   "score": 61.0,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge base en",
   "model_id": "baai/bge-base-en",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 60.8,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "jina embeddings v2 base en",
   "model_id": "jina/jina-embeddings-v2-base-en",
   "provider": "jina",
   "provider_display": "Jina AI",
   "score": 60.8,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "bge code v1",
   "model_id": "baai/bge-code-v1",
   "provider": "baai",
   "provider_display": "BAAI",
   "score": 60.5,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "nomic embed code",
   "model_id": "nomic/nomic-embed-code",
   "provider": "nomic",
   "provider_display": "Nomic AI",
   "score": 60.5,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "snowflake arctic embed m",
   "model_id": "snowflake/snowflake-arctic-embed-m",
   "provider": "snowflake",
   "provider_display": "Snowflake",
   "score": 60.5,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "e5 large unsupervised",
   "model_id": "intfloat/e5-large-unsupervised",
   "provider": "intfloat",
   "provider_display": "intfloat",
   "score": 60.2,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "jina embeddings v2 base de",
   "model_id": "jina/jina-embeddings-v2-base-de",
   "provider": "jina",
   "provider_display": "Jina AI",
   "score": 60.2,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Mistral Embed",
   "model_id": "mistral/mistral-embed",
   "provider": "mistral",
   "provider_display": "Mistral AI",
   "score": 60.2,
   "source": "mteb-leaderboard, miracl"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Qwen3 Embedding 0.6B",
   "model_id": "qwen/qwen3-embedding-0-6b",
   "provider": "qwen",
   "provider_display": "Alibaba / Qwen Team",
   "score": 60.0,
   "source": "mteb-leaderboard"
  },
  {
   "as_of": "2026-04",
   "attribution": "unverified-legacy",
   "display_name": "Qwen3 VL Embedding 2B",
   "model_id": "qwen/qwen3-vl-embedding-2b",
   "provider": "qwen",
   "provider_display": "Alibaba / Qwen Team",
   "score": 60.0,
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  {
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 ],
 "page": {
  "aliases": [
   "MTEB score",
   "MTEB Task Mean",
   "MTEB Task Type Mean"
  ],
  "category": "embedding",
  "contamination": {
   "note": "Most component datasets are public and some have been online since 2022, so a strong overall score partly reflects how much public test data a model's pretraining mix absorbed.",
   "risk": "medium"
  },
  "dataset": {
   "languages": [],
   "license": "Varies by underlying dataset; the MTEB benchmark code is Apache-2.0.",
   "modalities": [
    "text"
   ],
   "public_test_set": true,
   "size": null,
   "size_note": "Inherits its dataset from whichever MTEB variant is summarised: 58 datasets / 112 languages in the 2022 release; 41 tasks on today's MTEB(eng, v2) board and 131 tasks on MTEB(Multilingual, v2). See the mteb family page.",
   "splits": "",
   "url": "https://github.com/embeddings-benchmark/mteb"
  },
  "freshness": {
   "researched": "2026-09-07",
   "researched_by": "sonnet-5 agent, batch 1, slice D",
   "reviewed": "",
   "reviewed_by": ""
  },
  "harness": {
   "bigbench": "",
   "helm": "",
   "inspect_evals": "",
   "lm_eval": "",
   "opencompass": "",
   "other": "No standalone harness for the aggregate: run every task in the target MTEB benchmark variant via the mteb Python package (pip install mteb; mteb run) and take the mean it reports."
  },
  "id": "mteb_overall",
  "last_updated": "",
  "leaderboard_url": "https://huggingface.co/spaces/mteb/leaderboard",
  "lineage": {
   "family": "mteb",
   "predecessor": "",
   "successors": [],
   "variants": []
  },
  "measures": "mteb_overall is not a task of its own. It is the summary column the public MTEB leaderboard shows by default, combining every task type a given leaderboard variant covers \u2014 retrieval, classification, clustering, reranking, STS, summarization, pair classification, and on some variants bitext mining \u2014 into one number per model. It is what most vendor announcements and comparison charts quote when they say \"MTEB.\"\n",
  "metric": {
   "baseline_note": "No single random baseline: this is the mean of each task's own metric and each task keeps its own baseline.",
   "direction": "higher_is_better",
   "human_baseline": null,
   "max_score": 100,
   "name": "mean score (Task Mean or Task Type Mean)",
   "random_baseline": null,
   "unit": "%"
  },
  "name": "MTEB Overall (leaderboard average)",
  "page_kind": "benchmark",
  "paper": {
   "arxiv": "2210.07316",
   "title": "MTEB: Massive Text Embedding Benchmark",
   "url": "https://aclanthology.org/2023.eacl-main.148/",
   "year": 2023
  },
  "publisher": {
   "authors": [
    "Niklas Muennighoff",
    "Nouamane Tazi",
    "Lo\u00efc Magne",
    "Nils Reimers"
   ],
   "org": "Hugging Face and Cohere (original authors); maintained today by the open embeddings-benchmark community",
   "url": "https://github.com/embeddings-benchmark"
  },
  "released": "2022-10",
  "repo_url": "https://github.com/embeddings-benchmark/mteb",
  "saturation": {
   "as_of": "",
   "note": "Structurally harder to saturate than a single task, since a model must be near the ceiling on retrieval, clustering, STS and the rest simultaneously; still exposed to the family's contamination watch.",
   "status": "watch",
   "top_score": null
  },
  "sources": [
   {
    "accessed": "2026-09-07",
    "title": "MTEB: Massive Text Embedding Benchmark (arXiv preprint)",
    "url": "https://arxiv.org/abs/2210.07316"
   },
   {
    "accessed": "2026-09-07",
    "title": "MTEB: Massive Text Embedding Benchmark (ACL Anthology, EACL 2023)",
    "url": "https://aclanthology.org/2023.eacl-main.148/"
   },
   {
    "accessed": "2026-09-07",
    "title": "embeddings-benchmark/mteb GitHub repository",
    "url": "https://github.com/embeddings-benchmark/mteb"
   },
   {
    "accessed": "2026-09-07",
    "title": "MTEB Leaderboard (Hugging Face Space)",
    "url": "https://huggingface.co/spaces/mteb/leaderboard"
   }
  ],
  "status": "active",
  "subcategory": "text embedding (blended average)",
  "summary": "The blended average the public MTEB leaderboard shows across a model's task-type scores; the number most people mean when they say 'MTEB score'.",
  "tags": [
   "embedding",
   "leaderboard",
   "aggregate"
  ],
  "task_format": "Not a task: an aggregation (mean) of a model's scores on every task in a given MTEB benchmark variant, computed after the model has already been scored on each individual task.\n"
 }
}