---
title: "Reranking · Pharmaceuticals: model rankings and source evidence · Undominated.ai"
canonical: https://undominated.ai/rankings/reranking-pharmaceuticals/
description: "Reranking · Pharmaceuticals ranked by nDCG@10 × 100. Inspect exact model variants, source dates, measurement limits and downloadable evidence."
---

# Reranking · Pharmaceuticals: model rankings and source evidence · Undominated.ai

> Reranking · Pharmaceuticals ranked by nDCG@10 × 100. Inspect exact model variants, source dates, measurement limits and downloadable evidence.

[All rankings](/rankings/) / Rerankers

# Reranking · Pharmaceuticals

ViDoRe 3 reranking results from MTEB: English queries, the test split, and BM25s first-stage retrieval. Each subject has its own board.

**8 entries** · nDCG@10 × 100 · Higher is better

Source publication date not supplied. Retrieved 2026-11-05.

Source: [MTEB / embeddings-benchmark](https://github.com/embeddings-benchmark/results) · [CC0-1.0](https://github.com/embeddings-benchmark/results/blob/aee638c23eabc3300a1212087d1ea031a2280e76/LICENSE).

Point-estimate ordering. The source supplies no confidence bounds, so small score differences are not evidence of a decisive lead.

Reranking · Pharmaceuticals · nDCG@10 × 100

| Score rank | Model / evaluated variant | nDCG@10 × 100 |
| --- | --- | --- |
| 1 | nvidia/Nemotron-3.5-Rerank-8B-BF16 nvidia Checkpoint 708e844c3da0 | 71.128 |
| 2 | Qwen/Qwen3-VL-Reranker-8B Qwen Checkpoint 407993db318a | 69.828 |
| 3 | nvidia/llama-nemotron-rerank-vl-1b-v2 nvidia Checkpoint b8a9987b05b7 | 67.44 |
| 4 | KaLM-Embedding/KaLM-Reranker-V1-Large-R2 KaLM-Embedding Checkpoint a862dd8581d4 | 66.528 |
| 5 | Qwen/Qwen3-VL-Reranker-2B Qwen Checkpoint 4bd860ac4f15 | 66.027 |
| 6 | KaLM-Embedding/KaLM-Reranker-V1-Small-R2 KaLM-Embedding Checkpoint 68b74f09ff2a | 65.475 |
| 7 | KaLM-Embedding/KaLM-Reranker-V1-Nano-R2 KaLM-Embedding Checkpoint 32b242bb7fd7 | 64.317 |
| 8 | BAAI/bge-reranker-v2-m3 BAAI Checkpoint 953dc6f6f85a | 63.785 |

## What this ranking can tell you

 - These are document retrieval rerankers evaluated on a fixed candidate set, not a general language-model ranking.
- Coverage is limited to model revisions with this exact published task and English subset. Missing results are not scored.
- The source supplies no confidence interval here; the displayed rank orders point estimates and does not establish statistical significance.
- No result publication date is supplied. The retrieval date records when the pinned result files were checked.

## Check the source

The original evidence is retained byte for byte. The downloadable board includes the source URLs, revision and SHA-256 hashes.

[Download this board and provenance →](/data/rankings/reranking-pharmaceuticals.json)

 Source files and licences - [metadata · 067a6bdc988c](/data/rankings/evidence/067a6bdc988c1d557ca7213fc8d070cdf426514ef7cb1f907c6888186b9bf437.json) · [upstream source ↗](https://api.github.com/repos/embeddings-benchmark/results/commits/main)
- [licence · a2010f343487](/data/rankings/evidence/a2010f343487d3f7618affe54f789f5487602331c0a8d03f49e9a7c547cf0499.txt) · [upstream source ↗](https://raw.githubusercontent.com/embeddings-benchmark/results/aee638c23eabc3300a1212087d1ea031a2280e76/LICENSE)
- [model · 85b6e198dbde](/data/rankings/evidence/85b6e198dbde04d66a991ae919fef7d35ff29b4cad8ddaefaa6a04136ef64ae3.json) · [upstream source ↗](https://raw.githubusercontent.com/embeddings-benchmark/results/aee638c23eabc3300a1212087d1ea031a2280e76/results/BAAI__bge-reranker-v2-m3/953dc6f6f85a1b2dbfca4c34a2796e7dde08d41e/model_meta.json)
- [result · faf5eddedc03](/data/rankings/evidence/faf5eddedc038f047a8d03f4fb1cddc2f982218f101ec9cafe427d4fa62c78f6.json) · [upstream source ↗](https://raw.githubusercontent.com/embeddings-benchmark/results/aee638c23eabc3300a1212087d1ea031a2280e76/results/BAAI__bge-reranker-v2-m3/953dc6f6f85a1b2dbfca4c34a2796e7dde08d41e/Vidore3PharmaceuticalsBM25sReranking.v2.json)
- [model · 3de913c2af66](/data/rankings/evidence/3de913c2af6645bd25ff629b2880555d4e470eb91d549b14caa091b1b8a9ae88.json) · [upstream source ↗](https://raw.githubusercontent.com/embeddings-benchmark/results/aee638c23eabc3300a1212087d1ea031a2280e76/results/KaLM-Embedding__KaLM-Reranker-V1-Large-R2/a862dd8581d4cb1b686b5c595c08be405129656c/model_meta.json)
- [result · 546e2bfd8f2b](/data/rankings/evidence/546e2bfd8f2b3edb54555ffaa568516f723f3d02108bda0b30e29b5c227e5cf9.json) · [upstream source ↗](https://raw.githubusercontent.com/embeddings-benchmark/results/aee638c23eabc3300a1212087d1ea031a2280e76/results/KaLM-Embedding__KaLM-Reranker-V1-Large-R2/a862dd8581d4cb1b686b5c595c08be405129656c/Vidore3PharmaceuticalsBM25sReranking.v2.json)
- [model · 93dfadb32f8f](/data/rankings/evidence/93dfadb32f8f15055bfdba3d7b23da2bdb535e762e0ec167c7bc47c8fc8aa3dc.json) · [upstream source ↗](https://raw.githubusercontent.com/embeddings-benchmark/results/aee638c23eabc3300a1212087d1ea031a2280e76/results/KaLM-Embedding__KaLM-Reranker-V1-Nano-R2/32b242bb7fd7ad6fd404154c09560d249ffe017f/model_meta.json)
- [result · 1d8397a7846e](/data/rankings/evidence/1d8397a7846e374a755324d2a2d42ce7a76c49ccbc209e2dda42237929c0b01a.json) · [upstream source ↗](https://raw.githubusercontent.com/embeddings-benchmark/results/aee638c23eabc3300a1212087d1ea031a2280e76/results/KaLM-Embedding__KaLM-Reranker-V1-Nano-R2/32b242bb7fd7ad6fd404154c09560d249ffe017f/Vidore3PharmaceuticalsBM25sReranking.v2.json)
- [model · 25ef04976191](/data/rankings/evidence/25ef04976191e38617daf6c211c002e65070e2342d92d1abbac452bc4be3f6ed.json) · [upstream source ↗](https://raw.githubusercontent.com/embeddings-benchmark/results/aee638c23eabc3300a1212087d1ea031a2280e76/results/KaLM-Embedding__KaLM-Reranker-V1-Small-R2/68b74f09ff2ab5df9b9a9a48c812a81aaff8815e/model_meta.json)
- [result · a8ae7aeef6b7](/data/rankings/evidence/a8ae7aeef6b7ba8807f49fa3faec3583c4d38ad3c723e740786442a859af6d54.json) · [upstream source ↗](https://raw.githubusercontent.com/embeddings-benchmark/results/aee638c23eabc3300a1212087d1ea031a2280e76/results/KaLM-Embedding__KaLM-Reranker-V1-Small-R2/68b74f09ff2ab5df9b9a9a48c812a81aaff8815e/Vidore3PharmaceuticalsBM25sReranking.v2.json)
- [model · f1f3b963b2bb](/data/rankings/evidence/f1f3b963b2bb8c1a8d12bb6b8b506eee5abfecefd8c3b712b307e377c167a3a7.json) · [upstream source ↗](https://raw.githubusercontent.com/embeddings-benchmark/results/aee638c23eabc3300a1212087d1ea031a2280e76/results/Qwen__Qwen3-VL-Reranker-2B/4bd860ac4f15ad1897a214615cccc700f8f71818/model_meta.json)
- [result · 21ea4369671d](/data/rankings/evidence/21ea4369671d726028653be533eeff503a6ee830c80adce086985b0844c15c37.json) · [upstream source ↗](https://raw.githubusercontent.com/embeddings-benchmark/results/aee638c23eabc3300a1212087d1ea031a2280e76/results/Qwen__Qwen3-VL-Reranker-2B/4bd860ac4f15ad1897a214615cccc700f8f71818/Vidore3PharmaceuticalsBM25sReranking.v2.json)
- [model · 145589768b2b](/data/rankings/evidence/145589768b2b1eb97f9e04e7cd3257742402842fc8b2e77e9473393e9e543cd2.json) · [upstream source ↗](https://raw.githubusercontent.com/embeddings-benchmark/results/aee638c23eabc3300a1212087d1ea031a2280e76/results/Qwen__Qwen3-VL-Reranker-8B/407993db318a67dbb333bbeb8b1521fb703a3bb1/model_meta.json)
- [result · 36a004abc62c](/data/rankings/evidence/36a004abc62c2d8daf7dcee2c0dbb6c2e571e5cd4371f81c24c20891cbca2928.json) · [upstream source ↗](https://raw.githubusercontent.com/embeddings-benchmark/results/aee638c23eabc3300a1212087d1ea031a2280e76/results/Qwen__Qwen3-VL-Reranker-8B/407993db318a67dbb333bbeb8b1521fb703a3bb1/Vidore3PharmaceuticalsBM25sReranking.v2.json)
- [model · 0e19b8c1a685](/data/rankings/evidence/0e19b8c1a685ff712c71a798a251919e56c5285ab45b944520fc14b165d4bd2e.json) · [upstream source ↗](https://raw.githubusercontent.com/embeddings-benchmark/results/aee638c23eabc3300a1212087d1ea031a2280e76/results/nvidia__Nemotron-3.5-Rerank-8B-BF16/708e844c3da0bbaa923e9dc310b07da4ffb6a6b5/model_meta.json)
- [result · a8e935d65a57](/data/rankings/evidence/a8e935d65a575dbbb2c90f473871187e2acf384ca553f8a09672de8ad7d1bdd5.json) · [upstream source ↗](https://raw.githubusercontent.com/embeddings-benchmark/results/aee638c23eabc3300a1212087d1ea031a2280e76/results/nvidia__Nemotron-3.5-Rerank-8B-BF16/708e844c3da0bbaa923e9dc310b07da4ffb6a6b5/Vidore3PharmaceuticalsBM25sReranking.v2.json)
- [model · 09e36039fe33](/data/rankings/evidence/09e36039fe33ed0e700c432ebea1f9aef49bb2ef96d266ad7ac80b4a7a9124ad.json) · [upstream source ↗](https://raw.githubusercontent.com/embeddings-benchmark/results/aee638c23eabc3300a1212087d1ea031a2280e76/results/nvidia__llama-nemotron-rerank-vl-1b-v2/b8a9987b05b75db5ad949c825cbcbf6eb7c48b5a/model_meta.json)
- [result · 18917a9bfd7d](/data/rankings/evidence/18917a9bfd7d437f61e8f929a495e9242305275c7e9972af76ad7ae3e9904cd8.json) · [upstream source ↗](https://raw.githubusercontent.com/embeddings-benchmark/results/aee638c23eabc3300a1212087d1ea031a2280e76/results/nvidia__llama-nemotron-rerank-vl-1b-v2/b8a9987b05b75db5ad949c825cbcbf6eb7c48b5a/Vidore3PharmaceuticalsBM25sReranking.v2.json)

[Explore other model types →](/rankings/)[Read the benchmark guide →](/benchmarks/)

## Continue your investigation

 - [Understand the measurements](/benchmarks/)
- [Compare task performance](/best/)
- [Compare embedding models](/embeddings/)
- [Inspect ranking rules](/methodology/)