MTEB Model Discovery Report
Data Freshness : MTEB results dataset last updated on 2026-06-15.
Top Embedding Models for Code Search
Model
Code Search Score
General Retrieval Score
Params (M)
lightonai/LateOn-Code-edge
0.816549
nan
17
lightonai/LateOn-Code-edge-pretrain
0.791693
nan
16.798
thenlper/gte-small
0.781565
0.479423
33
avsolatorio/GIST-small-Embedding-v0
0.772521
0.480646
33.36
avsolatorio/NoInstruct-small-Embedding-v0
0.770071
0.488884
33.36
Model
Code Search Score
General Retrieval Score
Params (M)
lightonai/LateOn-Code
0.851318
nan
149
lightonai/LateOn-Code-pretrain
0.832574
nan
149.016
ibm-granite/granite-embedding-97m-multilingual-r2
0.799971
0.446515
97
avsolatorio/GIST-Embedding-v0
0.78981
0.503411
109.482
thenlper/gte-base
0.789403
0.496155
109
Model
Code Search Score
General Retrieval Score
Params (M)
geevec-ai/geevec-embeddings-1.0-lite
0.92365
0.53474
366
jinaai/jina-embeddings-v5-text-nano
0.90384
0.535934
239
microsoft/harrier-oss-v1-270m
0.89605
0.425505
270
Shuu12121/CodeSearch-ModernBERT-Crow-Plus
0.892957
nan
151.668
codefuse-ai/F2LLM-v2-330M
0.842182
0.475202
334
Model
Code Search Score
General Retrieval Score
Params (M)
voyageai/voyage-4-large
0.97726
nan
nan
voyageai/voyage-4-large (embed_dim=2048)
0.97719
nan
nan
google/gemini-embedding-2-preview
0.972905
nan
nan
microsoft/harrier-oss-v1-27b
0.96994
0.483455
27009.3
Octen/Octen-Embedding-8B-INT8
0.967965
nan
7567.3
How to Regenerate this Report This report was generated using the find_best_models.py script. To update it with the latest live data from MTEB, run:
uv run scripts/find_best_models.py --clear-cache --output MTEB-RANKINGS.md