The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining.
Exceptional Versatility: The embedding model has achieved state-of-the-art performance across a wide range of downstream application evaluations. The 8B size embedding model ranks No.1 in the MTEB multilingual leaderboard (as of June 5, 2025, score 70.58), while the reranking model excels in various text retrieval scenarios.
Comprehensive Flexibility: The Qwen3 Embedding series offers a full spectrum of sizes (from 0.6B to 8B) for both embedding and reranking models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, the embedding model allows for flexible vector definitions across all dimensions, and both embedding and reranking models support user-defined instructions to enhance performance for specific tasks, languages, or scenarios.
Multilingual Capability: The Qwen3 Embedding series offer support for over 100 languages, thanks to the multilingual capabilites of Qwen3 models. This includes various programming languages, and provides robust multilingual, cross-lingual, and code retrieval capabilities.
Model Overview
Qwen3-Embedding-0.6B-GGUF has the following features:
Model Type: Text Embedding
Supported Languages: 100+ Languages
Number of Paramaters: 0.6B
Context Length: 32k
Embedding Dimension: Up to 1024, supports user-defined output dimensions ranging from 32 to 1024
Quantization: q8_0, f16
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub.
MRL Support indicates whether the embedding model supports custom dimensions for the final embedding.
Instruction Aware notes whether the embedding or reranking model supports customizing the input instruction according to different tasks.
Our evaluation indicates that, for most downstream tasks, using instructions (instruct) typically yields an improvement of 1% to 5% compared to not using them. Therefore, we recommend that developers create tailored instructions specific to their tasks and scenarios. In multilingual contexts, we also advise users to write their instructions in English, as most instructions utilized during the model training process were originally written in English.
Usage
📌 Tip: We recommend that developers customize the instruct according to their specific scenarios, tasks, and languages. Our tests have shown that in most retrieval scenarios, not using an instruct on the query side can lead to a drop in retrieval performance by approximately 1% to 5%.
We advise you to clone llama.cpp and install it following the official guide. We follow the latest version of llama.cpp.
In the following demonstration, we assume that you are running commands under the repository llama.cpp.
./build/bin/llama-server -m model.gguf --embedding --pooling last -ub 8192 --verbose-prompt
📌 Tip: Qwen3 Embedding models default to using the last token as <|endoftext|>, so you need to manually append this token to the end of your own input context. In addition, when running the llama-server, you also need to manually normalize the output embeddings as llama-server currently does not support the --embd-normalize option.
Evaluation
MTEB (Multilingual)
Model
Size
Mean (Task)
Mean (Type)
Bitxt Mining
Class.
Clust.
Inst. Retri.
Multi. Class.
Pair. Class.
Rerank
Retri.
STS
NV-Embed-v2
7B
56.29
49.58
57.84
57.29
40.80
1.04
18.63
78.94
63.82
56.72
71.10
GritLM-7B
7B
60.92
53.74
70.53
61.83
49.75
3.45
22.77
79.94
63.78
58.31
73.33
BGE-M3
0.6B
59.56
52.18
79.11
60.35
40.88
-3.11
20.1
80.76
62.79
54.60
74.12
multilingual-e5-large-instruct
0.6B
63.22
55.08
80.13
64.94
50.75
-0.40
22.91
80.86
62.61
57.12
76.81
gte-Qwen2-1.5B-instruct
1.5B
59.45
52.69
62.51
58.32
52.05
0.74
24.02
81.58
62.58
60.78
71.61
gte-Qwen2-7b-Instruct
7B
62.51
55.93
73.92
61.55
52.77
4.94
25.48
85.13
65.55
60.08
73.98
text-embedding-3-large
-
58.93
51.41
62.17
60.27
46.89
-2.68
22.03
79.17
63.89
59.27
71.68
Cohere-embed-multilingual-v3.0
-
61.12
53.23
70.50
62.95
46.89
-1.89
22.74
79.88
64.07
59.16
74.80
Gemini Embedding
-
68.37
59.59
79.28
71.82
54.59
5.18
29.16
83.63
65.58
67.71
79.40
Qwen3-Embedding-0.6B
0.6B
64.33
56.00
72.22
66.83
52.33
5.09
24.59
80.83
61.41
64.64
76.17
Qwen3-Embedding-4B
4B
69.45
60.86
79.36
72.33
57.15
11.56
26.77
85.05
65.08
69.60
80.86
Qwen3-Embedding-8B
8B
70.58
61.69
80.89
74.00
57.65
10.06
28.66
86.40
65.63
70.88
81.08
Note: For compared models, the scores are retrieved from MTEB online leaderboard on May 24th, 2025.
MTEB (Eng v2)
MTEB English / Models
Param.
Mean(Task)
Mean(Type)
Class.
Clust.
Pair Class.
Rerank.
Retri.
STS
Summ.
multilingual-e5-large-instruct
0.6B
65.53
61.21
75.54
49.89
86.24
48.74
53.47
84.72
29.89
NV-Embed-v2
7.8B
69.81
65.00
87.19
47.66
88.69
49.61
62.84
83.82
35.21
GritLM-7B
7.2B
67.07
63.22
81.25
50.82
87.29
49.59
54.95
83.03
35.65
gte-Qwen2-1.5B-instruct
1.5B
67.20
63.26
85.84
53.54
87.52
49.25
50.25
82.51
33.94
stella_en_1.5B_v5
1.5B
69.43
65.32
89.38
57.06
88.02
50.19
52.42
83.27
36.91
gte-Qwen2-7B-instruct
7.6B
70.72
65.77
88.52
58.97
85.9
50.47
58.09
82.69
35.74
gemini-embedding-exp-03-07
-
73.3
67.67
90.05
59.39
87.7
48.59
64.35
85.29
38.28
Qwen3-Embedding-0.6B
0.6B
70.70
64.88
85.76
54.05
84.37
48.18
61.83
86.57
33.43
Qwen3-Embedding-4B
4B
74.60
68.10
89.84
57.51
87.01
50.76
68.46
88.72
34.39
Qwen3-Embedding-8B
8B
75.22
68.71
90.43
58.57
87.52
51.56
69.44
88.58
34.83
C-MTEB (MTEB Chinese)
C-MTEB
Param.
Mean(Task)
Mean(Type)
Class.
Clust.
Pair Class.
Rerank.
Retr.
STS
multilingual-e5-large-instruct
0.6B
58.08
58.24
69.80
48.23
64.52
57.45
63.65
45.81
bge-multilingual-gemma2
9B
67.64
75.31
59.30
86.67
68.28
73.73
55.19
-
gte-Qwen2-1.5B-instruct
1.5B
67.12
67.79
72.53
54.61
79.5
68.21
71.86
60.05
gte-Qwen2-7B-instruct
7.6B
71.62
72.19
75.77
66.06
81.16
69.24
75.70
65.20
ritrieve_zh_v1
0.3B
72.71
73.85
76.88
66.5
85.98
72.86
76.97
63.92
Qwen3-Embedding-0.6B
0.6B
66.33
67.45
71.40
68.74
76.42
62.58
71.03
54.52
Qwen3-Embedding-4B
4B
72.27
73.51
75.46
77.89
83.34
66.05
77.03
61.26
Qwen3-Embedding-8B
8B
73.84
75.00
76.97
80.08
84.23
66.99
78.21
63.53
Citation
If you find our work helpful, feel free to give us a cite.
@article{qwen3embedding,
title={Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models},
author={Zhang, Yanzhao and Li, Mingxin and Long, Dingkun and Zhang, Xin and Lin, Huan and Yang, Baosong and Xie, Pengjun and Yang, An and Liu, Dayiheng and Lin, Junyang and Huang, Fei and Zhou, Jingren},
journal={arXiv preprint arXiv:2506.05176},
year={2025}
}
