What is Tongyi Embedding Vision Flash?
Tongyi Embedding Vision Flash is a multimodal embedding model from Alibaba Cloud. An embedding is a numerical representation of content: the model converts an input such as a sentence, image, video, or image sequence into a vector that software can compare with other vectors. Similar content should produce vectors that are closer together, allowing applications to build semantic and cross-modal search systems.
The model is part of Alibaba Cloud's vision-centric Embedding-Vision family and is positioned as the lightweight Flash variant. Its output is a fixed 768-dimensional dense vector. The model does not generate prose, images, audio, or video; its job is to represent content for downstream retrieval and analysis.
Alibaba Cloud lists the model as available through Model Studio's International deployment in Singapore. The supplied lifecycle information gives a release date of October 31, 2025, and identifies the model as current and available through that deployment.
Supported inputs and outputs
Tongyi Embedding Vision Flash supports the following input types:
- Text, with a maximum length of 1,024 tokens.
- Images, supplied through public URLs or supported Base64 data URIs.
- Videos supplied through publicly accessible URLs.
- Multi-image sequences through the
multi_imagesinput type.
Documented image formats include JPG, PNG, and BMP. Documented video formats include MP4, MPEG, AVI, MOV, MPG, WEBM, FLV, and MKV. These inputs can be used independently or in supported combinations, but the model returns independent vectors rather than automatically merging all modalities into one fused representation.
The output is a dense 768-dimensional embedding. The dimension is fixed for this model, and the documented interface does not support changing it with a dimension parameter. Embedding output is not charged according to the supplied pricing information.
How the model works in practice
A typical workflow sends a collection of product images, media files, or text descriptions to the model and stores the returned vectors in a vector index. A search query is then embedded in the same representation space. For example, a text query can be compared with image vectors to support text-to-image search, while a reference image can be compared with catalog images for visual similarity.
Supported retrieval patterns include text-to-image, image-to-image, text-to-video, and video-to-video search. The model can also support image classification and multimedia catalog indexing when an application uses embedding similarity or a downstream classifier.
The distinction between independent and fused vectors is important. Independent vectors represent each submitted modality separately. They do not constitute a single vector that combines the meaning of a text description and an image into one fused representation. Applications that specifically require fused multimodal embeddings should evaluate a model designed for that purpose, such as Qwen3-VL-Embedding, rather than assuming that Tongyi Embedding Vision Flash performs fusion.
Technical specifications and limitations
| Specification | Documented value |
|---|---|
| Model type | Multimodal embedding model |
| Embedding dimension | 768, fixed |
| Maximum text length | 1,024 tokens |
| Input modalities | Text, images, videos, and multi-image sequences |
| Output | Dense independent vector embeddings |
| Configurable dimensions | Not supported |
| Text generation | Not supported |
| Image or video generation | Not supported |
The 1,024-token limit applies to text input. The supplied research does not specify a maximum video duration, maximum image size, maximum number of images in a sequence, or a maximum output-token setting. Those values should not be assumed from the text limit.
The model is not a reranker. An embedding model retrieves or compares candidates by representing them as vectors; a reranker is a separate component that can reorder retrieved results using a more detailed relevance assessment. Tongyi Embedding Vision Flash is also not a conversational model and should not be selected for answer generation or general-purpose assistant tasks.
Pricing
For the International Singapore deployment, Alibaba Cloud lists image and video input at $0.03 per 1 million input tokens and text input at $0.09 per 1 million input tokens. The supplied pricing information states that embedding output is free.
These prices are input-modality-specific, so a workload's cost depends on the mixture of text, images, and videos it processes. The published rates are particularly relevant for large media-indexing jobs, although an application should still account for storage, vector database, network, and any separate retrieval or reranking services.
Pricing and availability can vary by deployment and may change. The figures above describe the documented International Singapore deployment rather than every Alibaba Cloud region.
Strengths and trade-offs
The clearest practical strength is the combination of multimodal input support and a relatively compact fixed vector size. A 768-dimensional vector requires less storage and can reduce the computational burden of similarity search compared with a higher-dimensional representation, especially when a system indexes a large image or video library. This is an engineering trade-off rather than a provider-published benchmark result.
The model also covers several media types within one embedding family. A team building a multimedia catalog can use it for text, images, videos, and image sequences instead of maintaining unrelated representations for every input type. Its published image and video rate is lower than its text rate, which may be useful for media-heavy indexing workloads.
There are corresponding limitations. The output dimension cannot be configured, and independent vectors may not meet the needs of an application that requires a single fused representation across modalities. The model also provides no generative output, reasoning workflow, tool use, or reranking capability. Those functions would need to be supplied by other models or application components.
Reasoning, coding, and tool support
Tongyi Embedding Vision Flash is not a reasoning or coding model in the usual language-model sense. It transforms inputs into embeddings and does not produce explanations, program code, or conversational responses. The supplied model data assigns low reasoning and coding scores, but those are editorial evaluation fields, not claims published by Alibaba Cloud and not benchmark measurements.
The documented model profile does not list function calling, tool use, web search, streaming, structured output, or a batch API. These capabilities should therefore be treated as unsupported or unverified for this model rather than inferred from the broader Model Studio platform. Fine-tuning and caching are also not specified in the supplied research.
When to choose Tongyi Embedding Vision Flash
Choose this model when the main task is multimodal retrieval and the system benefits from a compact, fixed-size vector. Suitable examples include:
- Searching an image catalog with natural-language descriptions.
- Finding visually similar products or media assets.
- Searching video archives by text or comparing related videos.
- Indexing e-commerce, security, autonomous-driving, or other multimedia datasets.
- Building a cost-sensitive vector-search system where 768 dimensions are sufficient.
It is especially appropriate when the application needs independent representations for different media types and does not require the embedding model itself to generate answers. Its fixed output can make schema design straightforward and can help control vector storage at large scale.
When another option may be more appropriate
Use a generative language or vision-language model when the requirement is to explain an image, answer questions, write code, summarize a video, or conduct a conversation. Tongyi Embedding Vision Flash cannot perform those tasks.
Use a reranking service when first-stage vector retrieval is not enough and the application needs a separate relevance-ranking step. Use a fused multimodal embedding model when text and visual information must be represented together in one vector. The supplied research specifically identifies Qwen3-VL-Embedding as an example of a more appropriate direction for fused multimodal retrieval.
Finally, a higher-dimensional sibling may be preferable when the application has evidence that additional representation capacity improves its retrieval quality and the associated storage and search costs are acceptable. Alibaba Cloud's Tongyi Embedding Vision Plus is described as returning 1,152-dimensional vectors, compared with Flash's fixed 768 dimensions. The supplied research does not provide benchmark results showing how much quality differs, so the choice should be validated on the application's own data.
Bottom line
Tongyi Embedding Vision Flash is a focused embedding model for cost-conscious multimodal search. Its verified profile is straightforward: text, image, video, and multi-image inputs; a 1,024-token text limit; fixed 768-dimensional dense embeddings; and published Singapore pricing of $0.03 per million input tokens for image or video and $0.09 per million for text. It is a good fit for cross-modal retrieval and large media indexes, but not for generation, conversation, reranking, tool use, or fused multimodal representations.