mongodb
VoyageAI by MongoDB: voyage-4-lite
mongodb/voyage-4-lite
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
mongodb/voyage-4-lite is listed in the OpenRouter catalog as a text-to-embeddings model in MongoDB’s VoyageAI line. The catalog describes it as a lightweight, general-purpose embedding option designed for low latency and cost, using Matryoshka learning and quantization-aware training. Those are catalog-reported characteristics, not independently measured results. Because only this OpenRouter entry was supplied and no separate model card was available, suitability for a specific retrieval workload should be validated empirically.[1]
Strengths
Limitations
- Only an OpenRouter catalog entry was supplied; an independent model card was not available in this source pack.[1]
- The supplied source reports no measured benchmark results or evaluation settings, so retrieval quality, throughput, and real-world cost remain unverified.[1]
- The evidence does not document license terms, safety characteristics, language coverage, or hardware requirements.[1]
Where to use it
- A plausible candidate for semantic search, document indexing, or similarity matching when embedding latency and cost are important, subject to workload testing.[1]
- May suit general-purpose text-embedding pipelines that fit the catalog-listed context window, provided accuracy is validated on representative data.[1]
Sources
- [1] OpenRouter catalog · VoyageAI by MongoDB: voyage-4-liteSources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
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](https://www.mongodb.com/products/platform/ai-search-and-retrieval/models?utm_source=openrouter&utm_medium=referral)
Specifications
- Model ID
- mongodb/voyage-4-lite
- Canonical ID
- voyageai/voyage-4-lite-20260727
- Creator
- mongodb
- Added to OpenRouter
- 27.07.2026
- Context length
- 32,000
- Modality
- text->embeddings
- Input modalities
- text
- Output modalities
- embeddings
- Token pricing
- USD per 1M tokens: in $0.020 / out $0
- 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 | output: embeddings |
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 | $0.020 | $0 | — | — | 100% |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.