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 betterPage-level recall comparison across financial filings, industrial, pharmaceutical, and HR documents. TopK multi-vector uses topk-io/Iso-ModernColBERT model.
| Dataset / model | TopK Multi-Vector | Dense Embeddings (Qwen3 VLE 8B) |
|---|---|---|
| Financial filings | 80.9% | 58.9% |
| Pharmaceutical | 84% | 60% |
| HR | 81% | 68% |
| Industrial | 76% | 42% |
02
Agentic search
Better retrieval. Better agents.
BrowseComp-Plus
Accuracy and recall · higher is betterBrowseComp-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.
| Metric | TopK Multi-Vector | Dense Embeddings (Qwen3 8B) | BM25 |
|---|---|---|---|
| Accuracy | 80.48% | 71.69% | 57.59% |
| Recall | 77.82% | 78.98% | 61.7% |
03
Hybrid search
Combine multiple signals to optimize relevance.
Ranking signals working together
nDCG@10 · higher is betterTopK hybrid scoring compared with reciprocal rank fusion (RRF), measured by nDCG@10 on NFCorpus, FiQA, FEVER, and Natural Questions (NQ).
| Dataset / model | TopK Hybrid | Reciprocal Rank Fusion (RRF) |
|---|---|---|
| FEVER | 0.865 | 0.843 |
| NQ | 0.552 | 0.541 |
| FiQA | 0.429 | 0.415 |
| NFCorpus | 0.368 | 0.350 |
04
Billion documents in milliseconds
Predictable latency at scale with filtered or unfiltered queries.
View latency data
| Documents | p50 · No filter | p99 · No filter |
|---|---|---|
| 10M | 15.5 ms | 17 ms |
| 100M | 31.2 ms | 34.8 ms |
| 1B | 52.9 ms | 61.8 ms |
Latency measured on a production-config cluster with randomly generated documents and queries.
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