Best reranking model API
4 models · updated 2026-08-14
The verdict
Cohere Rerank leads — All 4 models rank Cohere Rerank the top pick.
As of 2026-08-14, ChatGPT, Claude, Gemini and Grok collectively rank Cohere Rerank #1 for reranking model api on ModelsAgree — a unanimous pick. The models' case: Best all-round production choice: excellent relevance quality, multilingual and structured-data support, 32K context, automatic long-document chunking, mature. The models' main caveat: Closed commercial model; Pro can be slower and costlier than leaner alternatives, so it is not ideal for strict data-sovereignty or self-hosting. The strongest alternative is Voyage Rerank — Near-tied with Cohere on general quality, with instruction-following, multilingual retrieval, 32K context, and especially strong value for. Source: https://modelsagree.com/best/best-reranking-model-api (modelsagree.com, CC BY 4.0).
Combined ranking
- 1GPT #1Claude #1Gemini #1Grok #1
Best all-round production choice: excellent relevance quality, multilingual and structured-data support, 32K context, automatic long-document chunking, mature integrations, and a faster sibling for latency-sensitive workloads
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GPT Best all-round production choice: excellent relevance quality, multilingual and structured-data support, 32K context, automatic long-document chunking, mature integrations, and a faster sibling for latency-sensitive workloads
Claude The category benchmark for a managed rerank API — strong cross-lingual quality across 100+ languages, long-context reranking, JSON/tabular field support, and a genuinely simple two-call RAG integration; enterprise deployment via AWS/Azure/OCI/Bedrock and private VPC makes it the safe default for teams that want quality without hosting.
Gemini Sets the industry standard for production RAG with unmatched ecosystem integration across vector databases and frameworks, superior handling of semi-structured/tabular data, robust multilingual support, and reliable low-latency managed SLAs at enterprise scale.
Grok Highest real-world production reliability and ecosystem integration (LangChain/LlamaIndex/Bedrock/Azure), 32k context, true 100+ language parity plus semi-structured/JSON support, and consistent top-tier relevance lifts on enterprise RAG workloads; the default safe choice when latency and quality both matter without self-hosting.
Where it falls shortper GPT Closed commercial model; Pro can be slower and costlier than leaner alternatives, so it is not ideal for strict data-sovereignty or self-hosting requirements
per Claude Closed weights and per-search pricing; you cannot self-host or fine-tune the ranker, and cost adds up at high query volume vs open models.
per Gemini Premium pricing at high query volumes and a closed-source model that prevents fully air-gapped local deployment outside cloud marketplace agreements.
per Grok Highest per-search cost among strong options and ~600 ms class latency that can dominate tight interactive budgets.
- 2GPT #2Claude #2Gemini #2Grok #3
Near-tied with Cohere on general quality, with instruction-following, multilingual retrieval, 32K context, and especially strong value for sophisticated RAG queries where ranking criteria need to be expressed in the query
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GPT Near-tied with Cohere on general quality, with instruction-following, multilingual retrieval, 32K context, and especially strong value for sophisticated RAG queries where ranking criteria need to be expressed in the query
Claude Consistently tops or matches Cohere on retrieval-quality evals with strong long-context and code/domain performance, pairs naturally with Voyage's leading embeddings for a matched retrieve+rerank stack, and MongoDB backing gives it staying power and Atlas integration.
Gemini Near-tie with Cohere on retrieval quality while consistently leading standard retrieval benchmarks (MTEB/BEIR) in technical, legal, and financial domains, offering exceptional precision per token and highly competitive API latency.
Grok Competitive or superior domain-tuned quality (code/finance/legal) with instruction-following, longer effective context than many peers, and pricing that scales better than Cohere at medium-high volume while matching latency.
Where it falls shortper GPT Smaller deployment and integration ecosystem than Cohere, making it less convenient for enterprises standardizing through multiple clouds and established search platforms
per Claude Smaller ecosystem and mindshare than Cohere; API-only with no self-host, and best value is realized mainly if you also adopt Voyage embeddings.
per Gemini Proprietary API-only deployment with narrower out-of-the-box ecosystem tooling compared to Cohere.
per Grok Shallower multilingual depth than Cohere and less mature multi-cloud availability.
- 3GPT #3Claude #3Gemini #3Grok #2
Best quality-per-parameter and listwise efficiency in 2026 (BEIR 63.20 nDCG@10, beating larger 4B models while ~7× smaller), hybrid attention giving 1.2–1.56× speedups on long candidates, strong structured/legal/medical gains, and both hosted API plus open weights for flexible deployment.
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Grok Best quality-per-parameter and listwise efficiency in 2026 (BEIR 63.20 nDCG@10, beating larger 4B models while ~7× smaller), hybrid attention giving 1.2–1.56× speedups on long candidates, strong structured/legal/medical gains, and both hosted API plus open weights for flexible deployment.
GPT Exceptional long-context and multilingual option with a 131K listwise architecture, open weights for non-commercial use, and an accessible hosted API; particularly strong for long documents and ranking candidates jointly rather than independently
Claude Best value-plus-flexibility pick — competitive multilingual and long-document quality, offered both as a cheap hosted API and as openly licensed weights you can self-host, so you can prototype on the API and move on-prem without changing model family.
Gemini Stands out for native 8k+ context length support, exceptional multilingual and code retrieval capabilities, highly competitive per-token pricing, and the ability to seamlessly switch between hosted API and open-weight self-hosting.
Where it falls shortper GPT CC-BY-NC licensing limits commercial self-hosting, while listwise processing can produce higher and less predictable latency
per Claude Peak accuracy trails Cohere/Voyage on the hardest domain benchmarks; the top-tier quality tier is where it's weakest, so quality-max users may outgrow it.
per Gemini Processing large 8k-token contexts introduces significant latency penalties, making it less suitable for ultra-tight, real-time SLA requirements without chunk truncation.
per Grok License friction on some weights for pure commercial self-host (API preferred) and candidate lists must fit the single long context window.
- 4GPT #4Claude #4Gemini #5Grok #4
Strong accuracy-per-dollar from a 1.5B multilingual model covering 100+ languages, with open weights and hosted access; a compelling choice when practitioners want API convenience without surrendering a future self-hosting path
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GPT Strong accuracy-per-dollar from a 1.5B multilingual model covering 100+ languages, with open weights and hosted access; a compelling choice when practitioners want API convenience without surrendering a future self-hosting path
Claude Strongest open-weight reranker family for self-hosters — Apache-licensed, competitive with commercial APIs on standard benchmarks, multiple size/latency tiers, and a hosted API option; the best merit-per-dollar when you control your own GPUs.
Grok Strong open-weights + managed API combination under Apache 2.0, solid BEIR/multilingual numbers, long context, and low self-host cost once GPU is available; excellent value when data residency or fine-control matters.
Gemini Exceptional developer experience with dedicated fast cross-encoders (mxbai-rerank), outstanding English and domain-specific retrieval precision, and extremely competitive cost-to-performance ratios on high-throughput workloads.
Where it falls shortper GPT Its hosted platform and operational track record are less mature than the leaders, and the more capable instruction-following v3 listwise model remains preview-grade
per Claude You own the serving, scaling, and latency engineering; without in-house inference ops the total cost and effort can exceed just paying Cohere/Voyage.
per Gemini Smaller enterprise footprint and narrower multilingual coverage compared to Cohere and Jina.
per Grok Not the absolute SOTA on every leaderboard and requires more operational effort than pure SaaS for peak reliability.
- 5GPT #5Claude #5Gemini #4Grok #5
The de-facto open-source cross-encoder standard (BGE-Reranker-v2-m3 / Large), delivering state-of-the-art cost efficiency, zero vendor lock-in, and total data privacy when hosted via modern serverless API endpoints.
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Gemini The de-facto open-source cross-encoder standard (BGE-Reranker-v2-m3 / Large), delivering state-of-the-art cost efficiency, zero vendor lock-in, and total data privacy when hosted via modern serverless API endpoints.
GPT Proven multilingual open-source cross-encoder with inexpensive managed serving, broad framework support, and the freedom to self-host; excellent practical value for ordinary short-query, passage-ranking pipelines
Claude The pragmatic open-source baseline everyone can run — permissive, well-documented, multilingual m3 variant, huge community and tooling support, trivially served via Text Embeddings Inference; hard to beat for a free, private, good-enough ranker.
Grok The most battle-tested lightweight multilingual open model (~0.6B, MIT), universal framework support, and best quality/latency/cost for self-hosted or high-volume pipelines where API spend must approach zero.
Where it falls shortper GPT The 1,024-token pair limit and older pointwise architecture make it materially weaker for long documents, nuanced instructions, and highest-accuracy workloads
per Claude Older architecture now behind mxbai-v2 and the commercial APIs on quality, and like all self-host options it offloads all infra and tuning onto you.
per Gemini Requires developer overhead to manage or configure inference infrastructure (e.g., TEI/vLLM) and lacks turn-key managed enterprise SaaS features.
per Grok Trails the newest proprietary and listwise models on absolute nDCG and lacks the instruction/listwise sophistication of 2026 leaders.
Rank history
Just missed the top 5
GPT ZeroEntropy zerank-2 — promising accuracy and aggressive pricing, but less independently validated and operationally proven than the top five · Pinecone rerank-v0 — convenient and fast inside Pinecone, but its 512-token limit and single-platform availability narrow its usefulness
Claude Together AI / Baseten hosted open rerankers — great for serving BGE/mxbai at scale without owning GPUs, but they're infrastructure for the models above, not a distinct ranker
Gemini ZeroEntropy Zerank API — Delivers cutting-edge speed and strong benchmark scores, but has a smaller track record in large-scale enterprise deployments
Grok Zerank-2 — topped some ELO boards and was extremely fast/cheap but API sunsets 2026-09-04 after Notion acquisition, making it unsuitable for new production · Qwen3-Reranker-4B — excellent open multilingual quality but higher self-host latency and less turnkey API maturity than the ranked options
By model
ChatGPT
- 1.Cohere Rerank
- 2.Voyage Rerank
- 3.Jina Reranker
- 4.Mixedbread Rerank
- 5.BGE Reranker
Claude
- 1.Cohere Rerank
- 2.Voyage Rerank
- 3.Jina Reranker
- 4.Mixedbread Rerank
- 5.BGE Reranker
Gemini
- 1.Cohere Rerank
- 2.Voyage Rerank
- 3.Jina Reranker
- 4.BGE Reranker
- 5.Mixedbread Rerank
Grok
- 1.Cohere Rerank
- 2.Jina Reranker
- 3.Voyage Rerank
- 4.Mixedbread Rerank
- 5.BGE Reranker
Common questions
What is the best reranking model api according to AI models?
Cohere Rerank leads. All 4 models rank Cohere Rerank the top pick. The current top 3: Cohere Rerank, Voyage Rerank, Jina Reranker. Ranked by asking ChatGPT, Claude, Gemini, Grok the same buying question and merging their top-5 picks, updated 2026-08-14. Source: modelsagree.com.
Which reranking model api did each AI model pick first?
ChatGPT: Cohere Rerank. Claude: Cohere Rerank. Gemini: Cohere Rerank. Grok: Cohere Rerank.
What changed in the latest reranking model api ranking?
In the latest poll (2026-08-14): BGE Reranker climbed 2 spots. The models are re-polled on demand, so this ranking moves.
How is this reranking model api ranking made?
ChatGPT, Claude, Gemini, Grok are each asked the same buying question in a fresh session with no system steering. Their top-5 answers are merged (rank 1 = 5 pts … rank 5 = 1 pt) into the consensus ranking, re-polled on demand and tracked over time.
More on how polling works: full methodology →
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Cite this ranking
ModelsAgree, “Best reranking model API” — merged ranking from ChatGPT, Claude, Gemini & Grok, polled 2026-08-14. https://modelsagree.com/best/best-reranking-model-api (CC BY 4.0)
Tracked by ModelsAgree · rank 1 = 5 pts … rank 5 = 1 pt · re-polled on demand