mongodb
VoyageAI by MongoDB: voyage-multimodal-3.5
mongodb/voyage-multimodal-3.5
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/voyage-multimodal-3.5 as a VoyageAI by MongoDB embedding model that converts multimodal inputs into vectors rather than generating text or images. Its structured fields list text and image inputs with embedding outputs, while the catalog description also describes video and interleaved text, image, and video content. The catalog provides a large context length but no measured benchmark results. Because this source pack has only catalog data and no independent model card, reported capabilities should be validated on representative workloads.[1]
Strengths
Limitations
- The only evidence is the OpenRouter catalog; an independent model card was not available in this source pack.[1]
- The catalog includes no measured benchmark results, so performance should be validated on representative workloads.[1]
- Structured fields list only text and image inputs even though the description mentions video; actual video support requires confirmation.[1]
Where to use it
- Consider for semantic retrieval or similarity search over text and image assets, pending workload tests.[1]
- May be evaluated for clustering or classification of multimodal content where embeddings are needed.[1]
- Potentially useful for indexing interleaved visual and textual material after confirming accepted modalities.[1]
Sources
- [1] OpenRouter catalog · VoyageAI by MongoDB: voyage-multimodal-3.5Sources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
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...
Specifications
- Model ID
- mongodb/voyage-multimodal-3.5
- Canonical ID
- voyageai/voyage-multimodal-3.5-20260727
- Creator
- mongodb
- Added to OpenRouter
- 27.07.2026
- Context length
- 32,000
- Modality
- text+image->embeddings
- Input modalities
- text, image
- 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 | input: image |
| 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.