All models
llm
Embedding model
embed-small
Text embeddings for search, clustering and retrieval; answers the OpenAI-compatible /v1/embeddings endpoint (never chat).
Decision and plan target (no provider attached) PII blocked by policy
- Price per 1M tokens
- $0.02
- $0.0000 per 1k
- Requests · 30d
- 0
- no traffic
- Latency
- 150 ms
- declared
- Success rate
- n/a
- quality prior 0.70
Traffic over 30 days
Requests per day this target answered on this deployment, with the cost line.
No requests yet. Route something from the playground to see usage here.
- Tokens
- 0
- Spend
- $0
- Previous window
- 0 req
Routed for
Domains the signal layer detected on requests that ended here.
No routed requests yet. Declared domains: General.
Capabilities
DomainsGeneral
ActionsEmbed
Complexity band0.00 - 1.00
Languages*
Context windowundeclared
Streamingyes
Tagsembeddings
Policy constraints
Hard stops enforced before scoring.
Data boundaryPublic
Regionsanywhere
PIInot allowed
Max input tokensunlimited
Tenantsall
Representative prompts
Examples the similarity strategy matches against.
- Embed these product descriptions for semantic search.
- Vectorise this paragraph.
Route to it
This llm is not executed by the platform. Ask the router for a decision restricted to kinds=["llm"] and dispatch the winner in your own stack; send feedback afterwards so the learners improve.
curlbash
curl https://api.opensmartroute.ai/api/v1/route \
-H "Authorization: Bearer $OSR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"text": "Embed these product descriptions for semantic search.", "kinds": ["llm"], "top_k": 3}'
# Returns the chosen target, confidence, the ranked alternatives and the
# per-strategy trace. Execution of llm targets happens in your stack.httpxpython
import httpx
r = httpx.post(
"https://api.opensmartroute.ai/api/v1/route",
headers={"Authorization": "Bearer osr_live_..."},
json={"text": "Embed these product descriptions for semantic search.", "kinds": ["llm"]},
)
d = r.json()
print(d["target"]["id"], d["confidence"])
print([t["id"] for t in d["ranked"]]) # e.g. embed-small first when it fits