MTEB Multilingual v2 leaderboard: who is #1

Name: MTEB Multilingual v2 rankings
Creator: MTEB
License: https://creativecommons.org/licenses/by/4.0/

MTEB · Mean task score, higher is better · 88 models · published Sep 22, 2026

MTEB

Top 25 of 88 models, best variant per model as published

#	Model	Score
1	harrier-oss-v1-27b (microsoft)as “microsoft/harrier-oss-v1-27b”	74.3%
2	KaLM-Embedding-Gemma3-12B-2511 (tencent)as “tencent/KaLM-Embedding-Gemma3-12B-2511”	72.3%
3	llama-embed-nemotron-8b (nvidia)as “nvidia/llama-embed-nemotron-8b”	69.5%
4	Qwen3-Embedding-8B (Qwen)as “Qwen/Qwen3-Embedding-8B”	70.6%
5	gemini-embedding-001 (google)as “google/gemini-embedding-001”	68.4%
6	Qwen3-Embedding-4B (Qwen)as “Qwen/Qwen3-Embedding-4B”	69.5%
7	Octen-Embedding-8B (Octen)as “Octen/Octen-Embedding-8B”	67.8%
8	F2LLM-v2-14B (codefuse-ai)as “codefuse-ai/F2LLM-v2-14B”	68.7%
9	F2LLM-v2-8B (codefuse-ai)as “codefuse-ai/F2LLM-v2-8B”	68.1%
10	harrier-oss-v1-0.6b (microsoft)as “microsoft/harrier-oss-v1-0.6b”	69.0%
11	Seed1.6-embedding-1215 (Bytedance)as “Bytedance/Seed1.6-embedding-1215”	70.3%
12	F2LLM-v2-4B (codefuse-ai)as “codefuse-ai/F2LLM-v2-4B”	67.1%
13	Giga-Embeddings-instruct-10B-A1.8B-0826 (ai-sage)as “ai-sage/Giga-Embeddings-instruct-10B-A1.8B-0826”	65.6%
14	jina-embeddings-v5-omni-small (jinaai)as “jinaai/jina-embeddings-v5-omni-small”	67.0%
14	jina-embeddings-v5-text-small (jinaai)as “jinaai/jina-embeddings-v5-text-small”	67.0%
16	F2LLM-v2-1.7B (codefuse-ai)as “codefuse-ai/F2LLM-v2-1.7B”	65.2%
17	BidirLM-Omni-2.5B-Embedding (BidirLM)as “BidirLM/BidirLM-Omni-2.5B-Embedding”	63.5%
18	Giga-Embeddings-instruct-3B-0826 (ai-sage)as “ai-sage/Giga-Embeddings-instruct-3B-0826”	63.9%
19	harrier-oss-v1-270m (microsoft)as “microsoft/harrier-oss-v1-270m”	66.5%
20	Qwen3-Embedding-0.6B (Qwen)as “Qwen/Qwen3-Embedding-0.6B”	64.3%
21	jina-embeddings-v5-omni-nano (jinaai)as “jinaai/jina-embeddings-v5-omni-nano”	65.5%
21	jina-embeddings-v5-text-nano (jinaai)as “jinaai/jina-embeddings-v5-text-nano”	65.5%
23	gte-Qwen2-7B-instruct (Alibaba-NLP)as “Alibaba-NLP/gte-Qwen2-7B-instruct”	62.5%
24	BidirLM-1.7B-Embedding (BidirLM)as “BidirLM/BidirLM-1.7B-Embedding”	63.5%
25	ICT-TIME-and-Querit-embedding-v1 (ICT-TIME-and-Querit)as “ICT-TIME-and-Querit/ICT-TIME-and-Querit-embedding-v1”	63.8%

About this board and its history

What it measures

Text embeddings across 131 tasks and 250+ languages: retrieval, clustering, classification, similarity.

Source

Scores as published by MTEB on the capture date (2026-09-24). Source: MTEB leaderboard backend API.

huggingface.co/spaces/mteb/leaderboard