v1.1 · Index update
3 models entered the index: Cohere Embed 5 Fast, Cohere Embed 5 Pro, Cohere Embed v4.0.
Embedding Benchmarks
v1.1Compare embedding models on 200 questions across finance, legal, technical support and healthcare. Explore AIMultiple's retrieval results, API prices and model details. See AIM Embeddings Index Methodology for further details.
Benchmark results through October 1, 2026. API prices checked November 4, 2026.
TOP MODEL
Voyage 4 Large
nDCG@10 0.851
BEST VALUE
PPLX Embed v1 0.6B
$0.1263 benchmark cost
LARGEST MODEL
PPLX Embed v1 4B
4.02B parameters
COVERAGE
25 models
4 domains · 200 questions
Benchmark Cost vs Retrieval Accuracy
Total cost of embedding all 341,040 passages and 200 queries for each model, against average nDCG@10. This is the cost of running the benchmark, not a price per million tokens. Estimated cost: Cohere Embed v4.0. The nDCG@10 axis starts at 0.50.
AIM Embedding Index
Leaderboard
All 25 models ranked by average nDCG@10 across finance, legal, technical support and healthcare.
Filter & Sort
# | Model | nDCG@10 | Finance | CUAD (legal) | TechQA (support) | MedRAG (healthcare) |
|---|---|---|---|---|---|---|
| 1 |  Voyage 4 Large Voyage AI | 0.851 | 0.838 | 0.704 | 0.887 | 0.978 |
| 2 |  Cohere Embed 5 Pro Cohere | 0.850 | 0.791 | 0.759 | 0.894 | 0.953 |
| 3 | Cohere Embed 5 Fast Cohere | 0.839 | 0.808 | 0.762 | 0.846 | 0.940 |
| 4 |  Gemini Embedding 2 Google | 0.830 | 0.824 | 0.731 | 0.796 | 0.967 |
| 5 | Voyage 4 Voyage AI | 0.829 | 0.782 | 0.700 | 0.855 | 0.980 |
| 6 | Voyage 4 Lite Voyage AI | 0.793 | 0.725 | 0.668 | 0.831 | 0.949 |
| 7 | Gemini Embedding 001 Google | 0.785 | 0.755 | 0.634 | 0.787 | 0.963 |
| 8 | Cohere Embed v4.0 Cohere | 0.772 | 0.723 | 0.626 | 0.785 | 0.953 |
| 9 | Voyage Code 4 Voyage AI | 0.770 | 0.824 | 0.687 | 0.616 | 0.952 |
| 10 |  Codestral Embed Mistral AI | 0.766 | 0.790 | 0.588 | 0.753 | 0.933 |
Page 1 of 3 | ||||||
Price ($/1M)-
Params-
ReleasedJul 2026
Finance
0.838
CUAD (legal)
0.704
TechQA (support)
0.887
MedRAG (healthcare)
0.978
Price ($/1M)-
Params-
ReleasedSep 2026
Finance
0.791
CUAD (legal)
0.759
TechQA (support)
0.894
MedRAG (healthcare)
0.953
Price ($/1M)-
Params-
ReleasedSep 2026
Finance
0.808
CUAD (legal)
0.762
TechQA (support)
0.846
MedRAG (healthcare)
0.940
Price ($/1M)$0.2000
Params-
ReleasedMay 2026
Finance
0.824
CUAD (legal)
0.731
TechQA (support)
0.796
MedRAG (healthcare)
0.967
Price ($/1M)-
Params-
ReleasedJul 2026
Finance
0.782
CUAD (legal)
0.700
TechQA (support)
0.855
MedRAG (healthcare)
0.980
Price ($/1M)-
Params-
ReleasedJul 2026
Finance
0.725
CUAD (legal)
0.668
TechQA (support)
0.831
MedRAG (healthcare)
0.949
Price ($/1M)$0.1500
Params-
ReleasedOct 2025
Finance
0.755
CUAD (legal)
0.634
TechQA (support)
0.787
MedRAG (healthcare)
0.963
Price ($/1M)-
Params-
ReleasedApr 2025
Finance
0.723
CUAD (legal)
0.626
TechQA (support)
0.785
MedRAG (healthcare)
0.953
Price ($/1M)-
Params-
ReleasedAug 2026
Finance
0.824
CUAD (legal)
0.687
TechQA (support)
0.616
MedRAG (healthcare)
0.952
Price ($/1M)$0.1500
Params-
ReleasedOct 2025
Finance
0.790
CUAD (legal)
0.588
TechQA (support)
0.753
MedRAG (healthcare)
0.933
Page 1 of 3
Embedding models benchmarks by release date
Each publisher's best average nDCG@10 by model release date. A step is a release that beat the publisher's earlier best; models without a release date are omitted. The chart shows 14 of 25 models.
AIM Embeddings Index Methodology
We benchmarked embedding models on a fixed set of 200 questions: 50 each in finance, legal, technical support and healthcare. Each query searches its domain's full collection of text passages. We rank models by mean nDCG@10, which rewards placing recorded relevant passages near the top of the first ten results.
The four collections contain 341,040 text passages: chunks extracted from source documents, not a count of sentences or original documents. The questions were selected using BM25, and relevance labels are incomplete. These are development-set results, not a blind holdout evaluation. Embeddings were generated through hosted APIs; the cost chart shows what embedding the full benchmark cost each model, and API list prices appear separately in the leaderboard.
Download all data
Raw results of 25 data points from the AIMultiple benchmarks this index is built from. Download them as a ZIP file containing one CSV file and a README.
Last updated: October 6, 2026
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Changelog
5 updates- v1.1
3 models entered the index: Cohere Embed 5 Fast, Cohere Embed 5 Pro, Cohere Embed v4.0.
- v1.1
Embedding Benchmarks: The index now uses one fixed run of 200 queries for every model, scored by nDCG@10. Version updated to v1.1.
- v1.0
2 models entered the index: Gemini Embedding 001, Gemini Embedding 2.
- v1.0
20 models entered the index: All-MPNet Base v2, BGE-M3 (dense), BGE Large EN v1.5, Codestral Embed, E5 Base v2 and 15 more.
- v1.0
Open-Source Embedding Models: Updated hardware, batch size, and precision in the Evaluation setup.
Explore Embeddings
Embedding Models: 25 Models Benchmarked for Retrieval
EmbeddingsBenchmarkOct 1We benchmarked 25 embedding models on 200 retrieval questions across finance, legal contracts, technical support and medical literature. The four corpora contained 341,040 text passages in total. Each query searched its own domain. nDCG@10 measures how highly the recorded relevant passages appear in the first ten results. Scores range from 0 to 1, with higher…
- EmbeddingsBenchmarkSep 27
Open Source Embedding Models: 11 Models Benchmarked for Retrieval
nDCG@10 scores the placement of recorded relevant passages within the first ten search results. A score of 1 represents the ideal ranking for the recorded positives. Passages nearer the top receive more weight. PPLX Embed v1 4B led the comparison at 0.741 nDCG@10, followed by PPLX Embed v1 0.6B at 0.698. Multilingual E5 Large ranked…
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