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title: MTEB-BR Leaderboard
emoji: 🏆
colorFrom: green
colorTo: yellow
sdk: static
pinned: true
license: apache-2.0
short_description: Massive Text Embedding Benchmark for Brazilian Portuguese
thumbnail: https://mteb-br.org/img/og.png
tags:
- leaderboard
- benchmark
- embeddings
- portuguese
- brazilian-portuguese
- mteb
- retrieval
- sentence-transformers
datasets:
- MTEB-BR/mteb-pt-results
models:
- Qwen/Qwen3-Embedding-8B
- tencent/KaLM-Embedding-Gemma3-12B-2511
- Octen/Octen-Embedding-8B
- Qwen/Qwen3-Embedding-4B
- Salesforce/SFR-Embedding-Mistral
- BidirLM/BidirLM-1.7B-Embedding
- ICT-TIME-and-Querit/BOOM_4B_v1
- google/embeddinggemma-300m
- Linq-AI-Research/Linq-Embed-Mistral
- jinaai/jina-embeddings-v5-text-small
- intfloat/multilingual-e5-large-instruct
- Salesforce/SFR-Embedding-2_R
- Alibaba-NLP/gte-Qwen2-7B-instruct
- microsoft/harrier-oss-v1-27b
- BidirLM/BidirLM-1B-Embedding
- codefuse-ai/F2LLM-v2-8B
- microsoft/harrier-oss-v1-0.6b
- codefuse-ai/F2LLM-v2-14B
- codefuse-ai/F2LLM-v2-4B
- SamilPwC-AXNode-GenAI/PwC-Embedding_expr
- Octen/Octen-Embedding-0.6B
- telepix/PIXIE-Rune-v1.0
- Qwen/Qwen3-Embedding-0.6B
- Alibaba-NLP/gte-Qwen2-1.5B-instruct
- Snowflake/snowflake-arctic-embed-l-v2.0
- BAAI/bge-m3
- codefuse-ai/F2LLM-v2-1.7B
- BidirLM/BidirLM-0.6B-Embedding
- ufca-llms/jua-4B-mixed
- codefuse-ai/F2LLM-0.6B
- microsoft/harrier-oss-v1-270m
- codefuse-ai/F2LLM-v2-0.6B
- intfloat/multilingual-e5-large
- ibm-granite/granite-embedding-311m-multilingual-r2
- ufca-llms/jua-4B-legal-only
- codefuse-ai/F2LLM-v2-330M
- intfloat/multilingual-e5-base
- PORTULAN/serafim-100m-portuguese-pt-sentence-encoder-ir
- PORTULAN/serafim-335m-portuguese-pt-sentence-encoder-ir
- intfloat/multilingual-e5-small
- ibm-granite/granite-embedding-107m-multilingual
- ibm-granite/granite-embedding-97m-multilingual-r2
- PORTULAN/serafim-900m-portuguese-pt-sentence-encoder-ir
- PORTULAN/serafim-335m-portuguese-pt-sentence-encoder
- PORTULAN/serafim-900m-portuguese-pt-sentence-encoder
- codefuse-ai/F2LLM-v2-160M
- sentence-transformers/paraphrase-multilingual-mpnet-base-v2
- PORTULAN/serafim-100m-portuguese-pt-sentence-encoder
- ufca-llms/Qwen3-Embedding-0.6B-jua-V2
- codefuse-ai/F2LLM-v2-80M
- sentence-transformers/LaBSE
- lfcc/medlink-bi-encoder
- intfloat/e5-small-v2
- mixedbread-ai/mxbai-embed-large-v1
- BAAI/bge-small-en-v1.5
- neuralmind/bert-large-portuguese-cased
- thenlper/gte-small
- neuralmind/bert-base-portuguese-cased
- stjiris/bert-large-portuguese-cased-legal-mlm-sts-v1.0
- intfloat/e5-mistral-7b-instruct
- nvidia/llama-embed-nemotron-8b
- rufimelo/Legal-BERTimbau-sts-large
- stjiris/bert-large-portuguese-cased-legal-mlm-mkd-nli-sts-v1
- Mihaiii/Ivysaur
- PORTULAN/albertina-900m-portuguese-ptbr-encoder
- sentence-transformers/all-MiniLM-L12-v2
- avsolatorio/GIST-all-MiniLM-L6-v2
- sentence-transformers/all-mpnet-base-v2
- ulysses-camara/legal-bert-pt-br
- sentence-transformers/all-MiniLM-L6-v2
- rufimelo/Legal-BERTimbau-sts-large-ma-v3
- stjiris/bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-MetaKD-v0
- sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
MTEB-BR — Brazilian Portuguese Massive Text Embedding Benchmark
A Massive Text Embedding Benchmark for Brazilian Portuguese — native, not translated.
- 93 models (73 open-weight + 20 closed commercial APIs) evaluated on 22 native Brazilian-Portuguese tasks across 7 categories
- Admits only data created or found in Portuguese; machine-translated benchmarks (e.g. mMARCO, mkqa) are excluded by construction
- Headline metric: mean_22 (average across all 22 tasks), reported with per-task bootstrap confidence intervals, paired-bootstrap significance, and IRT task discrimination
- Website: mteb-br.org · Paper: arXiv:2607.04581
- All raw results: MTEB-BR/mteb-pt-results · Source code: github.com/tardellirs/mteb-br
Tasks (22)
| Category | Tasks |
|---|---|
| Classification | HateBR, ToxSyn-PT, FactckBR, PortuLexRRIP |
| Multi-label classification | BrighterEmotion |
| Pair classification | ASSIN-RTE, InferBR |
| Semantic textual similarity (STS) | ASSIN-STS, ASSIN2-STS |
| Clustering | WikipediaPT-Categories, MedPT, JurisTCU, SciELO, StackOverflow-PT |
| Retrieval | Quati, JurisTCU, BRTaxQA, FaQuAD, MedPT, FaQ-Bacen |
| Reranking | Quati, JurisTCU |
How to add a model
Submit via GitHub Issues at tardellirs/mteb-br/issues with the model ID, evaluation JSONs, and a reproduction script.
Citation
@article{stekel2026mtebbr,
title = {MTEB-BR: A Text Embedding Benchmark for Brazilian Portuguese},
author = {Stekel, Tardelli Ronan Coelho},
journal = {arXiv preprint arXiv:2607.04581},
year = {2026}
}