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A reranker reorders the chunks your retriever returns so the best ones land on top - the cheapest upgrade to RAG accuracy. But the highest-scoring models often ship noncommercial weights, so “open” rarely means self-hostable. We ranked 12 on quality, price, speed, and license.
Best Reranker Models
#ModelBest for
How to Choose
When choosing between these models, consider:- Access: Decide first whether you’ll call a hosted API, use a managed platform, or self-host. Most of the strongest models are API-first; only some open-weight options are realistic to run yourself, and a few of those need a high-end GPU. That one choice drives cost, privacy, latency, and setup work.
- Quality: We use the Agentset Rerankers Leaderboard as the score, normalized to 0-100%. It ranks rerankers by head-to-head Elo from preference judgments on real retrieval tasks - a better proxy for “did it put the right chunk on top” than a single accuracy metric. A 0% is the bottom of the measured range, not a broken model, and three highlighted picks (Jina v3, Qwen3 0.6B, Nemotron) aren’t on the board yet.
- Price: We normalize to USD per 1M reranked tokens for one clean axis. Watch the fine print: Cohere and Voyage bill per search or request natively, so their per-token figures are conversions, and NVIDIA’s Nemotron has no public token price at all.
- Reranking Latency: Treat the millisecond figures as directional. Most come from Agentset’s hosted top-50 benchmark, but Jina v3, Qwen3 0.6B, and Nemotron use a different exact-model GPU benchmark, so they aren’t strictly comparable. Use latency mainly to separate “fast enough for live chat” from “batch only” - Qwen3 8B and Contextual v2 are firmly in the second group.
Other Models We Considered
Frequently Asked Questions
What's the best reranker for RAG right now?
What's the best reranker for RAG right now?
Zerank 2. It tops our leaderboard on ranking quality while staying among the fastest and cheapest hosted options. The catch is licensing: its open weights are noncommercial, so most teams use its metered API rather than self-hosting. If you want a fully commercial, closed managed service instead, Cohere Rerank 4 Pro is the closest rival.
What's the best reranker for most people?
What's the best reranker for most people?
For most RAG pipelines, Voyage Rerank 2.5 or Cohere Rerank 4 Pro are the safe managed defaults - high quality, long context, and no infrastructure to run. If cost matters more than the last few points of accuracy, Voyage 2.5 Lite and Qwen3 Reranker 0.6B are strong value picks.
What's the best open-source reranker I can self-host?
What's the best open-source reranker I can self-host?
For permissive, ship-it-anywhere weights, Qwen3 Reranker (0.6B on typical hardware, 8B if you have a GPU and can accept high latency) and BGE Reranker v2 M3 are the cleanest choices, all Apache 2.0. Zerank 1 Small is the fast, small option under the same terms. Watch out: several “open” rerankers, including Zerank 2, Jina v3, and Contextual v2, are noncommercial.
Do I even need a reranker?
Do I even need a reranker?
Usually, yes - reranking is often the cheapest way to lift answer quality, because it fixes the order of what you already retrieved. But it only helps when the right chunk is somewhere in your top results and just ranked too low. If recall is bad and the right chunk isn’t retrieved at all, fix retrieval first; a reranker can’t surface what isn’t there.
Is Cohere Rerank 4 better than Voyage Rerank 2.5?
Is Cohere Rerank 4 better than Voyage Rerank 2.5?
It depends on the metric. Cohere Rerank 4 Pro leads on preference-based ranking and covers more languages; Voyage 2.5 leads on pure retrieval-accuracy metrics and adds instruction-following, which Cohere lacks. Pick Cohere for broad multilingual enterprise content, Voyage when you want to steer ranking with instructions. They’re close enough to test both on your data.
No, and this is the biggest trap in the category. Several top open-weight rerankers - Zerank 2, Jina Reranker v3, Contextual v2 - ship under noncommercial licenses, so using the weights in a product needs a paid agreement. For unrestricted commercial self-hosting, stick to Apache 2.0 models like Qwen3, BGE v2 M3, and Zerank 1 Small.
Do reranker benchmarks match real-world use?
Do reranker benchmarks match real-world use?
Roughly, but not perfectly. Leaderboard rank tells you which models are contenders, yet the order shifts with your domain, language, and document length - and a model that tops academic tests, like Qwen3 8B, can land mid-pack on preference-based ranking. Treat the score as a shortlist filter, then measure your top two or three on your own queries.