Which LLM is the cheapest for our workload?

The best-for pages rank the live catalogue per task by the index that measures it, and the router picks the cheapest model that meets your quality rule for each request.

How much can we save on our LLM bill?

On the public benchmark the router saved 89% against sending everything to the reference model. Import last month's provider bill on the bill page and the estimate is computed on your own traffic before you change a line of code.

How often do model prices change?

The catalogue is refreshed from the vendors' price lists; each model page shows the date of its data.

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OpenSmartRoute

On-prem private model (llm-onprem) - OpenSmartRoute

All models

llm

On-prem private model

llm-onprem · runs as Llama 3.3 70B Instruct Meta

Self-hosted model for sensitive data that must not leave the boundary.

Chosen and quoted; no provider connected yet On Prem boundary May receive personal data

Try in playground JSON

Price per 1M tokens: $1.00; the target's declared price; the vendor's list price is below
Requests · 30d: 0; no traffic
Declared latency: 1.20 s; from the catalogue entry; nothing measured here in the window
Success rate: n/a; of the requests it answered; the quality prior is 70% (the router's starting estimate, not a benchmark)

Underlying model

Catalogue entry

Published by the vendor; the platform bills at the target's declared price.

Llama 3.3 70B Instruct

open weights tools

Meta meta-llama/llama-3.3-70b-instruct

The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model...

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, Finance, Legal, Medical, Hr, Customer Support.

Capabilities

DomainsGeneral, Finance, Legal, Medical, Hr, Customer Support

ActionsSummarize, Extract, Q&A, Classify, Generation

Complexity band0.00 - 0.70

Languages*

Context windowundeclared

Streamingyes

Policy constraints

Hard stops enforced before scoring.

Data boundaryOn Prem

RegionsEU, IN

PIIallowed

Max input tokensunlimited

Tenantsall

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://osr-api.kindrock-caaab1a2.centralindia.azurecontainerapps.io/api/v1/route \
  -H "Authorization: Bearer $OSR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"text": "Extract the account number and email from this support ticket.", "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://osr-api.kindrock-caaab1a2.centralindia.azurecontainerapps.io/api/v1/route",
    headers={"Authorization": "Bearer osr_live_..."},
    json={"text": "Extract the account number and email from this support ticket.", "kinds": ["llm"]},
)
d = r.json()
print(d["target"]["id"], d["confidence"])
print([t["id"] for t in d["ranked"]])  # e.g. llm-onprem first when it fits

One target among many