How much does rerank-2.5 cost?

rerank-2.5 costs $0.05/M input tokens.

How much text can rerank-2.5 process at once?

rerank-2.5 accepts up to 32,000 tokens per query and document. Longer text has to be split into multiple documents.

What inputs and outputs does rerank-2.5 support?

rerank-2.5 accepts text as input and returns relevance-ranked documents.

Which providers serve rerank-2.5?

rerank-2.5 is served by 1 provider on OpenRouter: Voyage AI by MongoDB.

What other reranking models does MongoDB have?

rerank-3-lite, rerank-3, and rerank-2.5-lite are other reranking models from MongoDB.

When was rerank-2.5 released?

rerank-2.5 was released on July 27, 2026.

VoyageAI by MongoDB: rerank-2.5

mongodb/rerank-2.5

rerank-2.5 is a cutting-edge reranker optimized for quality, delivering a 7.94% improvement in retrieval accuracy over Cohere Rerank v3.5 across 93 datasets. It also outperformed Cohere Rerank v3.5 by 12.70% on the Massive Instructed Retrieval Benchmark (MAIR). The model supports a combined context length of 32K tokens per query–document pair, including up to 8K tokens for the query, enabling more accurate retrieval over longer documents. Additionally, rerank-2.5 supports instruction following, allowingbout rerank-2.5 here: mongodb.com/products/platform/ai-search-and-retrieval/models

Modalities

Price

$0.05/M tokens

Context

32K

Released

Jul 27, 2026

Providers

This model is hosted by one provider. OpenRouter forwards every request without routing.

Latency


	$0.05	0.16s	100.00%

Input $0.05
0.16s latency100.00% uptime

Pricing

The average price customers actually pay for this model, next to the prices providers post. Caching and discounts mean the price actually paid is often well below the listed one.

Weighted average

Input

$0/M tokens

Output

$0/M tokens

rerank-2.5 — Price history

Price history

No effective price data for this selection

		$0.00	$0.00	0.0%	100.0%

Effective in $0.00Effective out $0.00
Token share
100.0%

Uptime

Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.

Uptime (3d)

100.00%

Availability (3d)

99.99%

Availability over the last 3 days

Last 72 hours

Availability 99.99%

3 Days Ago2 Days AgoYesterdayNow

Availability over the last 24 hours

OpenRouter Availability

99.99%

When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and provider routing options.

Not currently available through EU in-region routing. In-region routing docs

More models from MongoDB

rerank-3-lite

rerank-3-lite is a reranker optimized for both latency and quality and a drop-in upgrade to rerank-2.5-lite, improving on it by 0.94% NDCG@10 on average across domain evaluations and by 1.86% on long-document evaluations, with code retrieval gains of 2.77% atop voyage-3-large and 2.59% atop voyage-4-large. It matches the retrieval quality of rerank-2.5, and across 93 retrieval datasets it outperforms Cohere Rerank v4.0 Pro by 1.44% and Qwen3-Reranker-8B by 2.61%. The model supports a combined context length of 32K tokens per query-document pair, including up to 8K tokens for the query, enabling more accurate retrieval over longer documents. Additionally, rerank-3-lite supports instruction following, allowing users to guide relevance scoring t rerank-3-lite here: mongodb.com/products/platform/ai-search-and-retrieval/models

Rerank$0.02/M tokens

rerank-3

rerank-3 is a reranker optimized for quality and a drop-in upgrade to rerank-2.5, improving on it by 0.80% NDCG@10 on average across domain evaluations and by 3.35% on long-document evaluations, with code retrieval gains of 2.01% atop voyage-3-large and 1.96% atop voyage-4-large. Across 93 retrieval datasets it outperforms Cohere Rerank v4.0 Pro by 2.14% and Qwen3-Reranker-8B by 3.31%. The model supports a combined context length of 32K tokens per query-document pair, including up to 8K tokens for the query, enabling more accurate retrieval over longer documents. Additionally, rerank-3 supports instruction following, allowing users to guide relevance sco about rerank-3 here: mongodb.com/products/platform/ai-search-and-retrieval/models

Rerank$0.05/M tokens

voyage-code-4

voyage-code-4 is a code embedding model from Voyage AI, a MongoDB company. It is designed for coding agents and code retrieval, with Matryoshka embeddings at 2048t voyage-code-4 here: mongodb.com/products/platform/ai-search-and-retrieval/models

Embeddings$0.12/M tokens

rerank-2.5-lite

rerank-2.5-lite is a reranker optimized for both latency and quality, delivering a 7.16% improvement in retrieval accuracy over Cohere Rerank v3.5 across 93 datasets. It also outperformed Cohere Rerank v3.5 by 10.36% on the Massive Instructed Retrieval Benchmark (MAIR). The model supports a combined context length of 32K tokens per query–document pair, including up to 8K tokens for the query, enabling more accurate retrieval over longer documents. Additionally, rerank-2.5-lite supports instruction following, allowrerank-2.5-lite here: mongodb.com/products/platform/ai-search-and-retrieval/models

Rerank$0.02/M tokens

voyage-multimodal-3.5

voyage-multimodal-3.5 is a state-of-the-art multimodal embedding model capable of vectorizing not only text, images, and video individually, but also content that interleaves all three modalities. It delivers excellent performance for mixed-modality searches involving text and visual content such as PDF screenshots, figures, tables, videos, and more. Enabled by Matryoshka learning and quantization-aware training, voyage-multimodal-3.5 supports embeddings in 2048, 1024, 512, and 256 dimen-multimodal-3.5 here: mongodb.com/products/platform/ai-search-and-retrieval/models

Embeddings$0.60/B pixels

voyage-4-lite

voyage-4-lite is a lightweight, general-purpose embedding model optimized for low latency and cost. Enabled by Matryoshka learning and quantization-aware training, voyage-4-lite supports embeddings t voyage-4-lite here: mongodb.com/products/platform/ai-search-and-retrieval/models

Embeddings$0.02/M tokens

voyage-4

voyage-4 is a general-purpose (including multilingual) embedding model optimized for retrieval/search and AI applications. voyage-4 supports embedd about voyage-4 here: mongodb.com/products/platform/ai-search-and-retrieval/models

Embeddings$0.06/M tokens

voyage-4-large

voyage-4-large is a state-of-the-art general-purpose and multilingual embedding optimized for retrieval quality. Enabled by Matryoshka learning and quantization-aware training, voyage-4-large supports embeddings voyage-4-large here: mongodb.com/products/platform/ai-search-and-retrieval/models

Embeddings$0.12/M tokens

VoyageAI by MongoDB: rerank-2.5

Providers