Benchmarks.

Fast and accurate retrieval is the backbone of the new generation of AI-native applications.
TopK is built for teams working with large volumes of data where high performance, flexibility, and cost efficiency matter.

01

Enterprise retrieval

Find the details that dense embeddings miss.

Accuracy across real-world domains

Recall · higher is better
TopK Multi-VectorDense Embeddings (Qwen3 VLE 8B)

Page-level recall comparison across financial filings, industrial, pharmaceutical, and HR documents. TopK multi-vector uses topk-io/Iso-ModernColBERT model.

Dataset / modelTopK Multi-VectorDense Embeddings (Qwen3 VLE 8B)
Financial filings80.9%58.9%
Pharmaceutical84%60%
HR81%68%
Industrial76%42%

02

Agentic search

Better retrieval. Better agents.

BrowseComp-Plus

Accuracy and recall · higher is better
TopK Multi-VectorDense Embeddings (Qwen3 8B)BM25

BrowseComp-Plus with GPT-5 agent and the default retrieval/eval harness. Compares the TopK Semantic Index (multi-vector), Qwen3 Embedding 8B (dense), and BM25. Official result accepted by the benchmark leaderboard.

MetricTopK Multi-VectorDense Embeddings (Qwen3 8B)BM25
Accuracy80.48%71.69%57.59%
Recall77.82%78.98%61.7%

03

Hybrid search

Combine multiple signals to optimize relevance.

Ranking signals working together

nDCG@10 · higher is better
TopK HybridReciprocal Rank Fusion (RRF)

TopK hybrid scoring compared with reciprocal rank fusion (RRF), measured by nDCG@10 on NFCorpus, FiQA, FEVER, and Natural Questions (NQ).

Dataset / modelTopK HybridReciprocal Rank Fusion (RRF)
FEVER0.8650.843
NQ0.5520.541
FiQA0.4290.415
NFCorpus0.3680.350

04

Billion documents in milliseconds

Predictable latency at scale with filtered or unfiltered queries.

Query latency

p50p99
LOWER IS BETTER
View latency data
Documentsp50 · No filterp99 · No filter
10M15.5 ms17 ms
100M31.2 ms34.8 ms
1B52.9 ms61.8 ms

Latency measured on a production-config cluster with randomly generated documents and queries.

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