Voyage AI by MongoDB

📢 Introducing rerank-3 and rerank-3-lite, the next generation of Voyage rerankers.

A drop-in upgrade to the Rerank 2.5 series models, with focused improvements on long document and code retrieval.

📈 On standard retrieval, rerank-3 outperforms Cohere Rerank v4.0 Pro by 2.72% and Qwen3-Reranker-8B by 3.02%.

📚 On long document retrieval, rerank-3 outperforms rerank-2.5 by 3.35% and Cohere Rerank v4.0 Pro by 13.84%.

💰 rerank-3-lite now matches the retrieval quality of rerank-2.5 at 40% of the price.

Try it today. The first 200M tokens are free. mongodb.social/6016BGsoXp

“Voyage wins on score, cost, and latency.” Don’t believe us? Check out the latest benchmark from @kapa_ai.

Frontier models keep getting better, but they aren’t the whole stack. Agentic systems constantly make smaller decisions: what to retrieve, where to route a query, and which results matter. Specialized models are often better suited to that work.

Jev, @typesafeai’s general-purpose decision model, is a great example. Give it context, a question, and a set of labels, and it returns a probability for each label, allowing you to steer it with a rubric for routing, labeling, or ranking.

@kapa_ai tested Jev as a reranker on 1,000 real-world evaluation cases built from production queries over company knowledge bases, including product docs, Slack threads, support tickets, wikis, and code. Jev wasn’t built for reranking, yet it came within a hair of first place on retrieval quality, while also beating several dedicated rerankers.

Which model finished first? The rerank-3 model from Voyage AI by MongoDB topped the benchmark for retrieval quality while also being the cheapest and fastest model that Kapa measured.

Read the post for the full setup and results: mongodb.social/6016BHno7j

Voyage AI by @MongoDB’s Rerank 3 series is now available on @vercel AI Gateway.

Why use a reranker? Search finds candidates. Rerankers bring the most relevant context to the top for your agents. Better context, better answers.

🎯 rerank-3: optimized for retrieval accuracy when precision matters.

⚡ rerank-3-lite: optimized for latency and cost when speed is most important.

Learn more at mongodb.social/6011BG7TRB

Today, we're excited to announce the public preview of data residency in Europe for the Atlas Embedding and Reranking API Service (ERAS).

European teams can now use Voyage AI embedding and reranking models through @MongoDB Atlas while keeping inference within the selected Europe geography—helping organizations meet data-residency requirements while building accurate AI-powered search and retrieval. mongodb.social/6010B1Af7A

We've partnered with @FireworksAI_HQ to bring Voyage AI models to their dedicated inference platform.

That means embed, retrieve, rerank, and generate all in one place, right alongside the open models you're already running. mongodb.social/6012B1suFy