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NoJunk LLM Embeddings (BGE-M3)
x402
NoJunk LLM Embeddings (BGE-M3) is a paid API for AI agents from nojunk-llm.mick8511.workers.dev, paid per call via x402, $0.001/call, status unknown (last checked 2026-10-16).
Generates 1024-dimensional vector embeddings for up to 32 text strings per call using BGE-M3, paid per-request in USDC with no API key required.
Price / call$0.001
Success—
StatusUnknown
How this service works
Pay-per-call, OpenAI-compatible LLM API for agents: POST /v1/chat/completions (Llama 3.3 70B or Qwen 2.5 Coder 32B, up to 32,000 characters of messages, up to 1,024 output tokens, optional JSON mode) and POST /v1/embeddings (BGE-M3, 1,024 dimensions, up to 32 inputs per call). Point any OpenAI client at this base URL and pay per request in USDC on Base via x402: no API key, no account, no rate limits. You are only charged when a valid result is returned.
Output
Returns a JSON object conforming to the OpenAI embeddings response format: a list of embedding objects each containing the embedding index, a 1024-dimensional float array, and the object type 'embedding'; plus the model name ('bge-m3') and token usage stats (prompt_tokens and total_tokens).
Example use cases
RAG document ingestion pipeline
“Embed these 10 product review snippets so I can store them in my vector database for semantic search — use BGE-M3 and give me back the 1024-dimensional vectors for each.”
Semantic similarity scoring
“I have two customer support tickets: 'My order never arrived' and 'Package not delivered to my address' — embed both so I can compute how semantically similar they are and decide if they should be merged.”
Zero-signup embeddings for a prototype
“I'm prototyping a search feature and need embeddings for these 5 article titles right now without setting up an account — just embed them with BGE-M3 and charge me per call in USDC.”
Technical details
Endpoint
POSThttps://nojunk-llm.mick8511.workers.dev/v1/embeddings?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need BGE-M3 embeddings (multilingual, strong cross-lingual retrieval) with zero onboarding friction — no API key, no account, no rate limits — and you want to pay only when a valid result is returned, billed per-call in USDC on Base via x402. Ideal for agents, prototypes, or low-volume workloads where provisioning a traditional embedding API account is overkill. Also suitable when your stack already uses an OpenAI-compatible client and you just need to swap the base URL.
Side effects
- Input array exceeds 32 strings — request rejected
- Individual string exceeds 8000 characters — may be truncated or rejected
- Payment via x402 fails or USDC balance insufficient — no charge, no result returned
- Invalid JSON body or missing 'input' field — request fails
- Model field set to unsupported model name — falls back or errors
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| input | — | A string, or an array of 1-32 strings (each 1-8000 characters) |
| model | string | Optional: bge-m3 (the only embedding model) |
Output
Schema · Response
{
"type": "json",
"example": {
"data": [
{
"index": 0,
"object": "embedding",
"embedding": [
0.0213,
-0.0471,
0.0088,
0.0329
]
},
{
"index": 1,
"object": "embedding",
"embedding": [
0.0213,
-0.0471,
0.0088,
0.0329
]
}
],
"model": "bge-m3",
"usage": {
"total_tokens": 11,
"prompt_tokens": 11
},
"object": "list"
}
}Tags
x402
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