mongodb
VoyageAI by MongoDB: voyage-4-large
mongodb/voyage-4-large
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
voyage-4-large is a text-to-embeddings model listed in the OpenRouter catalog under VoyageAI by MongoDB. Per the catalog description, it is a general-purpose, multilingual embedding model aimed at retrieval quality, built with Matryoshka learning and quantization-aware training. The listing reports a 32,000-token context length, text-only input, and embedding output. All capability claims here are vendor-reported through the catalog; no independent model card or measured benchmark results were included in this source pack, so the retrieval-quality positioning remains unverified in this evidence.[1]
Strengths
- Catalog describes a general-purpose, multilingual embedding model positioned for retrieval quality.[1]
- Reported use of Matryoshka learning and quantization-aware training may allow flexible embedding sizes and efficient deployment, though specifics are truncated in the source.[1]
- 32,000-token context length is reported, which is comparatively long for embedding inputs.[1]
Limitations
- Only an OpenRouter catalog entry was supplied; no independent model card was available in this source pack, so capabilities need validation on your own workloads.[1]
- No benchmark results or third-party measurements were included; retrieval quality is a vendor claim, not a measured result.[1]
- Output is embeddings only and no reasoning capability is reported; it is not a text-generation model.[1]
Where to use it
- Worth evaluating for semantic search and retrieval over multilingual document collections, given its stated positioning.[1]
- A candidate for RAG indexing pipelines that ingest long documents, pending workload testing.[1]
- May suit deployments seeking dimension-flexible or quantized embeddings via Matryoshka-style training, if confirmed in testing.[1]
Sources
- [1] OpenRouter catalog · VoyageAI by MongoDB: voyage-4-largeSources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
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](https://www.mongodb.com/products/platform/ai-search-and-retrieval/models?utm_source=openrouter&utm_medium=referral)
Specifications
- Model ID
- mongodb/voyage-4-large
- Canonical ID
- voyageai/voyage-4-large-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 $0.120 / 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 | $0.120 | $0 | — | — | 100% |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.