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llm-small · runs as GPT-4.1 Nano OpenAI
Cheap, fast generalist for simple chat, short answers, classification and extraction.
Published by the vendor; the platform bills at the target's declared price.
OpenAI openai/gpt-4.1-nano
For tasks that demand low latency, GPT‑4.1 nano is the fastest and cheapest model in the GPT-4.1 series. It delivers exceptional performance at a small size with its 1 million...
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.
Domains the signal layer detected on requests that ended here.
No routed requests yet. Declared domains: General, General Chat, Customer Support.
Hard stops enforced before scoring.
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.
curl https://osr-api.kindrock-caaab1a2.centralindia.azurecontainerapps.io/api/v1/route \
-H "Authorization: Bearer $OSR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"text": "Hi, how are you today?", "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.import httpx
r = httpx.post(
"https://osr-api.kindrock-caaab1a2.centralindia.azurecontainerapps.io/api/v1/route",
headers={"Authorization": "Bearer osr_live_..."},
json={"text": "Hi, how are you today?", "kinds": ["llm"]},
)
d = r.json()
print(d["target"]["id"], d["confidence"])
print([t["id"] for t in d["ranked"]]) # e.g. llm-small first when it fitsPhoto: Varun Bhatheja
Cached input reads $0.03 per 1M
Examples the similarity strategy matches against.
The catalogue prints input and output prices and what three typical monthly workloads cost on each.
Free plan, no card. Fifteen thousand decisions a month with the full trace.
