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Watt Multimodal Embeddings (Qwen3-VL)
x402
Watt Multimodal Embeddings (Qwen3-VL) is a paid API for AI agents from watt.area21.io, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-21).
Generates 2048-dimensional embeddings for text and/or images using Qwen3-VL-Embedding-2B on Watt's own fleet, billed per call in USDC via x402.
Price / call$0.002
Success—
StatusUnknown
Output
Returns an OpenAI-compatible embeddings response containing one embedding object per input item. Each embedding is a float array (or base64 string) of the requested dimensionality (64–2048, default 2048), along with usage statistics (prompt tokens) and the model ID used.
Example use cases
Multimodal product catalog indexing
“I need to embed all my product listings for semantic search — each item has a title like 'Red wool scarf, merino blend' and a public image URL. Can you create 1024-dimensional vectors for each, with the instruction set to 'represent products for similarity retrieval'?”
RAG pipeline document vectorization
“Embed this paragraph for my knowledge base retrieval system: 'The transformer architecture relies on self-attention mechanisms to model long-range dependencies.' Use 2048 dimensions and set the instruction to 'represent passages for question answering retrieval'.”
Image-only semantic search index
“I want to generate embeddings for a batch of 10 images — here are their public HTTPS URLs — so I can do visual similarity search. Use the default Qwen3-VL model and return float vectors at 512 dimensions.”
Technical details
Endpoint
POSThttps://watt.area21.io/v1/embeddings?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need multimodal embeddings that handle both text and images in a single vector — especially useful for vision-language retrieval, product search with images, or any RAG pipeline that indexes mixed content. Prefer it over text-only embedding APIs when your data includes images or image-text pairs. The OpenAI-compatible API format makes it a drop-in replacement with USDC pay-per-call pricing via x402, requiring no subscription.
Side effects
- Missing required 'input' field returns a validation error
- Image URLs that are not publicly accessible will fail to fetch
- Batch exceeds 256 items limit returns an error
- Dimensions outside 64–2048 range are rejected
- Payment failure via x402 (insufficient USDC balance) blocks the request
- Unsupported model ID causes an error
- Invalid base64 or data URL for images causes a parsing error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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