mongodb
VoyageAI by MongoDB: rerank-2.5-lite
mongodb/rerank-2.5-lite
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
OpenRouter’s catalog presents mongodb/rerank-2.5-lite as a text-to-rerank model intended to balance latency and retrieval quality. It lists text input, reranking output, a 32,000-token context length, and the canonical slug voyageai/rerank-2.5-lite-20260727. Catalog copy reports an improvement over Cohere Rerank v3.5 across 93 datasets, but this source pack contains no independent model card or reproduced benchmark. Treat those claims as catalog information until validated on the target workload.[1]
Strengths
Limitations
- The only supplied evidence is the OpenRouter catalog; an independent model card was not available in this source pack.[1]
- The reported advantage over Cohere Rerank v3.5 is catalog-reported performance, not independently measured here.[1]
- Supported request parameters are not listed, and knowledge cutoff and reasoning fields are unset, so workload validation is required.[1]
Where to use it
- Candidate for evaluation in retrieval pipelines that need text result reordering without text generation.[1]
- Potentially useful where latency matters, since the catalog positions the model for latency and quality.[1]
- Worth testing on longer candidate records if the listed 32,000-token context meets workload needs.[1]
Sources
- [1] OpenRouter catalog · VoyageAI by MongoDB: rerank-2.5-liteSources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
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...
Specifications
- Model ID
- mongodb/rerank-2.5-lite
- Canonical ID
- voyageai/rerank-2.5-lite-20260727
- Creator
- mongodb
- Added to OpenRouter
- 27.07.2026
- Context length
- 32,000
- Modality
- text->rerank
- Input modalities
- text
- Output modalities
- rerank
- Token pricing
- No per-token price in the source snapshot — see OpenRouter for the billing unit
- Knowledge cutoff
- —
- Expiration date
- —
Tasks and capabilities
A grade appears only where the source supplies a matching measurement for that task, the model is active and the cohort holds at least 10 comparable models. The capability column lists facts the source declares — supported formats, reasoning and tool parameters — which are not measured quality.
| Task | Grade | Source-declared capability |
|---|---|---|
| General quality | No measurement | — |
| Coding | No measurement | — |
| Agents | No measurement | — |
| Writing | No measurement | — |
| Reasoning | No measurement | — |
| Document analysis | No measurement | — |
| Vision | No measurement | — |
| Image generation | No measurement | — |
| Audio | No measurement | — |
| Video | No measurement | — |
| Embeddings | No measurement | — |
Evaluations
No evaluations are supplied for this model.
How grades work
Each letter grade compares a model only against other models scored by the same source and metric in the current snapshot. S means the top 10%, A the next 20%, B the next 30%, C the rest. Models without a published measurement are shown as not rated — a supported format alone never implies measured quality.
- S — S — top 10% of the cohort
- A — A — next 20% of the cohort
- B — B — next 30% of the cohort
- C — C — below the B band
Providers
Optional live provider statistics from OpenRouter. Missing values are unknown, not zero.
| Provider | Context length | Price in /1M | Price out /1M | Latency p50 (30m, s) | Throughput p50 (30m, tok/s) | Uptime (30m) |
|---|---|---|---|---|---|---|
| VoyageAI by MongoDB | 32,000 | — | — | — | — | 100% |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.