Cost estimator

Know what a request costs before you send it.

Paste a prompt. The router estimates tokens, scores every target and quotes each one with its price split into input and output, expected quality and latency, then names the recommended, cheapest capable, best-quality and fastest picks.

Your request

Nothing is executed and the prompt is not stored. Tokens are estimated from the text; costs use each model's list price.

Know the price before you send

The router reads your prompt, estimates tokens, scores every model, agent and skill in the catalogue and quotes each one with its expected quality and latency.

Recommended models

What the router picks per use case

Live quotes of representative prompts against this deployment's catalogue, recomputed every minute. Rankings follow the targets, list prices and learned quality, not a hand-maintained list.

Everyday assistant

17 candidates

Small fast model

Qwen3.5 4Bllm

per request: $0.000083
routing quality: Routing quality score: the router's own estimate of how well this target would answer this prompt (0-1), from its quality prior and learned outcomes - not an independent benchmark.: 0.72
est. latency: Estimated latency: the target's declared or observed time to answer, as the router weighs it for this prompt - not a standardised benchmark.: 1.6 min

Why: domain fit 50% · action fit 50% · complexity fit 100%

CheapestMid-tier model$0.04/1M
Best qualitythis pick
FastestResearch agent$6.00/1M

Coding and refactoring

17 candidates

Small writing model

Qwen3.5 4Bllm

per request: $0.000013
routing quality: Routing quality score: the router's own estimate of how well this target would answer this prompt (0-1), from its quality prior and learned outcomes - not an independent benchmark.: 0.75
est. latency: Estimated latency: the target's declared or observed time to answer, as the router weighs it for this prompt - not a standardised benchmark.: 2.8 min

domain fit 50% · complexity fit 100% · quality prior 0.60

Analysis and reasoning

17 candidates

Frontier reasoning model

GLM 4.7 Flashllm

per request: $0.00012
routing quality: Routing quality score: the router's own estimate of how well this target would answer this prompt (0-1), from its quality prior and learned outcomes - not an independent benchmark.: 0.67
est. latency: Estimated latency: the target's declared or observed time to answer, as the router weighs it for this prompt - not a standardised benchmark.: 2.3 min

domain fit 67% · complexity fit 100% · quality prior 0.93

Summaries and rewriting

17 candidates

Summarization skill

skill-summarizeskill

per request: $0.00017
routing quality: Routing quality score: the router's own estimate of how well this target would answer this prompt (0-1), from its quality prior and learned outcomes - not an independent benchmark.: 0.71
est. latency: Estimated latency: the target's declared or observed time to answer, as the router weighs it for this prompt - not a standardised benchmark.: 1.20 s

Extraction and classification

17 candidates

Small fast model

Qwen3.5 4Bllm

per request: $0.000033
routing quality: Routing quality score: the router's own estimate of how well this target would answer this prompt (0-1), from its quality prior and learned outcomes - not an independent benchmark.: 0.72
est. latency: Estimated latency: the target's declared or observed time to answer, as the router weighs it for this prompt - not a standardised benchmark.: 1.6 min

domain fit 33% · complexity fit 100% · quality prior 0.55

Sensitive data

13 candidates

On-prem private model

Qwen3.5 4Bllm

per request: $0.00023
routing quality: Routing quality score: the router's own estimate of how well this target would answer this prompt (0-1), from its quality prior and learned outcomes - not an independent benchmark.: 0.84
est. latency: Estimated latency: the target's declared or observed time to answer, as the router weighs it for this prompt - not a standardised benchmark.: 16.9 s

Most recommended across use cases

1
Small fast model
router's pick for 2 use cases · cheapest 1x · best quality 1x
llm
2
On-prem private model
router's pick for 1 use case · cheapest 1x · best quality 2x
llm
3
Small writing model
router's pick for 1 use case · cheapest 3x · best quality 1x

Before the bill, not after

A quote is a number you can put in front of someone before the money is spent.

Every figure on this page is the same one the router uses when it decides: list prices per token, the tokens your prompt will take, the quality each target has earned on requests like it. Paste the prompt your team sends most, and the difference between the model you use today and the cheapest one that would do is on the screen in a second.

Photo: Jakub Zerdzicki

Quote from code

The same quote is one call away: anonymous quotes are rate limited per client, keyed quotes apply your tenant rules and show up in your usage.

POST /api/v1/estimatebash

curl -s /api/v1/estimate -H "Authorization: Bearer $OSR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"text": "Summarise this contract...", "monthly_requests": 50000}'

Or from the SDK and your IDE

osr estimate quotes against a local catalogue and the MCP server exposes estimate and recommend tools to VS Code, Cursor and Claude.

opensmartroute.estimatepython

from opensmartroute.estimate import estimate

q = estimate(router, "Summarise this contract...", output_tokens=300)
print(q.recommended.name, q.recommended.cost_usd, q.savings_usd)

Set up the IDE integration under Integrations or read the MCP guide.