Which reranker fits your scenario?

Decision guide · ~8 min read · Updated 12 Oct 2026

Rerankers are not one-size-fits-all. The right choice depends on language coverage, latency budget, data sensitivity, and whether your queries look more like support tickets, legal clauses, code search, or RAG chunks.

Scenario matrix

Each pick links to its page or row, where the vendor’s own figures are shown with how they were measured. None of them replaces a test on your own queries — see evaluating a reranker.

RAG & knowledge bases

Retrieve 50–100 chunks, rerank to 5–10, then prompt. Without a GPU, bge-reranker-v2-m3 is the usual self-hosted default; with one, Qwen3-Reranker-4B, which in Qwen’s own tests beats the 8B on English. As a hosted API, Cohere Rerank 4 bills per search and Voyage rerank-3 per token — the cost calculator shows which is cheaper for your volume. See rerank for RAG and hybrid retrieval when lexical matches matter.

Rule of thumb: if bi-encoder recall is already good, a mid-size reranker is enough. If recall is messy, fix retrieval width first.

Chinese and multilingual

The classic small models — ms-marco MiniLM, Jina v1, mxbai v1 — were trained on English only. Qwen3-Reranker (100+ languages), bge-reranker-v2-m3 and the 306M gte-multilingual-reranker-base (70+ languages) were trained for it. If your servers are in mainland China and you would rather not host a model, Alibaba Cloud Model Studio sells qwen3-rerank by the token in yuan, and SiliconFlow serves bge-reranker-v2-m3 and Qwen3-Reranker behind one endpoint; both are in the models table and the cost calculator’s yuan table. To download open weights in mainland China, hf-mirror.com mirrors Hugging Face. The mainland China guide compares the three routes in detail.

Customer support

Queries are short, passages are FAQ-sized, and latency dominates UX. A tiny cross-encoder (Jina v1 tiny, ms-marco MiniLM) often suffices for English — you are choosing among a handful of similar answers, not reading 100-page PDFs. For other languages, start with bge-reranker-v2-m3. Try the Customer support preset in our demo.

Legal & compliance

Distractors share vocabulary (“termination”, “notice period”, “breach”). Bi-encoders confuse them; cross-encoders separate nuance, and an instruction-following reranker lets you say what counts — “the clause that governs termination for convenience”, “later amendments supersede earlier ones”. Qwen3-Reranker and Voyage rerank-3 take instructions; Contextual AI Rerank v2’s weights are non-commercial. Budget extra latency for a larger model and keep passages to single clauses. Demo preset: Legal clause.

Code & technical docs

Vector search alone misses exact function names; BM25 alone misses paraphrased “how do I…” questions. Use hybrid retrieval, then rerank. Qwen publish code-retrieval scores for Qwen3-Reranker (see the Qwen page), and Voyage report rerank-3’s gain over rerank-2.5 on their code datasets (see the Voyage page). Demo presets: Technical docs and Code search.

Scanned PDFs, slides and screenshots

If your documents are page images — scans, slide decks, dashboards — a text reranker only sees whatever OCR extracted. Qwen3-VL-Reranker (2B / 8B) and LightOn-rerank score page images directly, both under Apache 2.0, and Alibaba Cloud sells qwen3-vl-rerank as an API. Their published scores are on ViDoRe, a visual-document benchmark, so compare them with each other rather than with the BEIR column.

Try it in the demo

Each scenario below maps to a built-in preset — load it, hit Rerank, and watch the bi-encoder proxy column mis-order before the cross-encoder fixes it.

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