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 quickly does routing pay back?

There is nothing to pay back: the free plan and the open-source router cost nothing to start, and the first routed request already carries its own cost against the baseline.

How do we keep AI spend under control by team?

Each workspace, each tenant and each API key can carry a daily or monthly budget and a request quota; the router enforces them and the dashboard shows spend against the line as it happens.

90 days of the enterprise, pro and team plans free, no card needed. See the plans

OpenSmartRoute

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.

Recommendedrouting quality 0.71

On-prem private model

Qwen3.5 4B

$0.00023per request · 1.20 s

$2.35 / month at 10K requests

Cheapest capablerouting quality 0.65

Small writing model

Qwen3.5 4B

$0.0000117per request · 1.5 min

$0.1175 / month at 10K requests

Best qualityrouting quality 0.71

On-prem private model

Qwen3.5 4B

$0.00023per request · 1.20 s

$2.35 / month at 10K requests

Fastestrouting quality 0.71

On-prem private model

Qwen3.5 4B

$0.00023per request · 1.20 s

$2.35 / month at 10K requests

What the router saw

35 tokens in, 200 out (predicted); confidence 22%.

rewritingcomplexity 0.26reasoning 0.08customer_supportfinance3 excluded by policy

Routing saves about $1.2K a month

Sending every request to the most expensive option would cost $1.2K for 10K requests; the recommended pick costs $2.35 for a total of 2.4M tokens.

All 22 candidates

Ranked by the router's utility; cost splits input and output tokens at list price. The routing quality score is the router's own estimate for this prompt (its prior for the model, adjusted by learned outcomes), on a 0-1 scale - not an independent benchmark.

#	Target	Input	Output	Per request	Routing quality	Latency
1	On-prem private modelllmRecommendedBest qualityFastest domain fit 100% · complexity fit 100% · quality prior 0.70	$0.0000350	$0.00020	$0.00023	0.71	1.20 s
2	Small writing modelllmCheapest capable domain fit 50% · complexity fit 100% · quality prior 0.60	$0.0000017	$0.0000100	$0.0000117	0.65	1.5 min
3	Customer support agentagent rule 'support-domain' prefers · domain fit 50% · action fit 0%	$0.0000700	$0.00040	$0.00047	0.65	1.50 s
4	Frontier reasoning modelllm domain fit 50% · complexity fit 64% · quality prior 0.93	$0.0000021	$0.0000800	$0.0000821	0.63	31 min
5	Small fast modelllm domain fit 50% · complexity fit 100% · quality prior 0.55	$0.0000070	$0.0000400	$0.0000470	0.62	31 min
6	Mid-tier modelllm domain fit 50% · complexity fit 100% · quality prior 0.75	$0.0000006	$0.0000180	$0.0000186	0.60	9.1 min
7	gpt-oss-20b (self-hosted)llm domain fit 50% · complexity fit 86% · quality prior 0.78	$0.0000006	$0.0000180	$0.0000186	0.55	34 min
8	Document and image reader (OCR)llm domain fit 100% · action fit 0% · complexity fit 100%	$0.0000035	$0.0000200	$0.0000235	0.51	1.50 s
9	gpt-oss-20b (ollama-gpt-oss-20b)llm domain fit 50% · action fit 50% · complexity fit 100%	$0.0000006	$0.0000180	$0.0000186	0.53	29 min
10	Translation skillskill domain fit 50% · action fit 0% · complexity fit 100%	$0.0000175	$0.00010	$0.00012	0.49	400.0 ms
11	Summarization skillskill domain fit 50% · action fit 0% · complexity fit 100%	$0.0000280	$0.00016	$0.00019	0.49	1.20 s
12	Browser and desktop automation agentagent domain fit 100% · action fit 0% · complexity fit 100%	$0.00017	$0.00100	$0.00118	0.51	12.0 s
13	Customer notification workflowworkflow domain fit 50% · action fit 0% · complexity fit 100%	$0	$0	$0.00400	0.53	2.00 s
14	B Calculatortool domain fit 0% · action fit 0% · complexity fit 100%	$0	$0	$0	0.43	5.0 ms
15	B Web searchtool domain fit 50% · action fit 0% · complexity fit 100%	$0	$0	$0	0.45	1.20 s
16	SRE and observability agentagent domain fit 0% · complexity fit 100% · quality prior 0.84	$0.00028	$0.00160	$0.00188	0.48	9.00 s
17	SQL skillskill domain fit 0% · action fit 0% · complexity fit 100%	$0.0000350	$0.00020	$0.00023	0.44	700.0 ms
18	Coding agentagent domain fit 0% · action fit 0% · complexity fit 94%	$0.0000021	$0.0000800	$0.0000821	0.45	6.00 s
19	Research agentagent domain fit 50% · action fit 0% · complexity fit 79%	$0.00021	$0.00120	$0.00141	0.44	8.00 s
20	Code modelllm domain fit 0% · action fit 0% · complexity fit 100%	$0.0000021	$0.0000800	$0.0000821	0.40	26.6 s
21	H Human escalation queuehuman domain fit 100% · action fit 0% · complexity fit 100%	$0.0175	$0.1000	$0.1175	0.47	5.0 min
22	Image generatortool domain fit 0% · action fit 0% · complexity fit 100%	$0.00140	$0.00800	$0.00940	0.37	9.00 s

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

Mid-tier model

gpt-oss-20bllm

per request: $0.000036
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.

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.

For the business

What it does to the AI bill.

89% less
Lower cost per request
On the 150-prompt benchmark the router spent 89% less than sending every prompt to the reference model, and the judged quality went up, not down.
Read the benchmark
From last month's bill
Your own estimate
The calculator works from your real volumes and models, not an assumed mix.
Calculate your savings
Budgets per workspace, tenant and key
A bill you can forecast

Quote first. Spend second.

A quote needs no account. When the number is right, the same request goes through the router with one more field - and comes back with the model, the price and the reason.

Quote a request now Create a free account

Know what a request costs before you send it.

Your request

What the router saw

All 22 candidates

What the router picks per use case

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

Quote from code

Or from the SDK and your IDE

What it does to the AI bill.

Lower cost per request

Your own estimate

A bill you can forecast

Quote first. Spend second.

Most recommended across use cases