Cheapest that passes your bar

Spend less on AI without gambling on quality

Find out which models are actually good enough for your work, then run every job on the cheapest provider that clears that bar. You see the cost before anything starts, and pay for what actually ran.

  • No signup to get a price
  • No subscriptions or minimums
  • No provider lock-in

Where the money goes

per $100 today
  • Standard real-time rate

    What the same work costs you today

    $100.00
  • Provider batch pricing−50%

    Providers price batch work near half of standard

    $50.00
  • Native-lane discount−10%

    On OpenAI and Anthropic lanes

    $45.00
  • Routed to the week's floor−12%

    Cheapest provider that clears your bar

    $39.60
  • Batchrouter service fee+5.90%

    Routing, retries, delivery, settlement

    +$2.34
You pay$41.94

Illustrative — modelled from published provider batch rates and the Batchrouter fee schedule. Your number depends on your models, your volume, and the week.

≈50%
Provider batch rates vs. standard, same models
−10%
Further off native OpenAI and Anthropic lanes
Actual usage
Estimates reserve; the receipt charges what ran
24h SLA
Fee refunded if we miss the window
Compared on every batch
The economics

Where the savings come from

Four effects that compound — none of which ask you to change a prompt, accept worse output, or rewrite anything.

Batch what doesn't need an answer this second

Nightly enrichment, classification queues, document pipelines, backfills, labelling. Providers price this work far below their standard rates. The output is identical; the bill is not.

Compare providers on every run, not once

Equivalent-tier models sit 8–25% apart on price, and the cheaper one changes from week to week. Wire up a single provider and you are locked to whatever it charges. We re-check every batch.

Only count savings that survive your quality bar

A cheaper model that gets it wrong is not cheaper. Score the candidates on your own data first, then let price decide among the ones that passed.

Pay for what actually ran

You see the cost before work starts, and we settle against real usage at the end. Anything reserved and unspent goes straight back. No subscriptions, no minimums, no tiers.

Evaluation

Cheap only counts if it is good enough

Public benchmarks are run on someone else's data. Score the candidates on yours, set the bar where your business needs it, and let price decide among the models that cleared it.

  • Bring a sample of your own work, and what a good answer looks like.
  • Every candidate provider runs it at once — one job, one price, one settlement.
  • Grading happens on our side and costs you nothing. You pay only for the inference.
  • A model has to prove it clears your bar, not merely average out at it.
ScorecardYour bar: 0.92
ModelScoreCost / 1kVerdict
Sonnet-class0.96$2.41Passes — 2.8× the price
Haiku-class0.94$0.86Cheapest passing — routed here
Mini-class0.91$0.71Below your bar
Open-weights 70B0.78$0.34Below your bar

The cheapest model on the board is not the one we pick. The cheapest one that cleared your bar is.

Illustrative scorecard — your models, scores, and costs come from your own run.

Try before you commit

Start with a price, not a migration

Size the savings before you talk to anyone, and prove the quality before you move a single production job.

step 1

Price it

Put your real volume into the calculator and see what the work would cost across providers. No account, no card.

step 2

Prove it

Run one sweep on your own data to find out which models are actually good enough — and what each of them really costs.

step 3

Route it

Set your bar once. Every batch after that goes to the cheapest provider that clears it, delivered within 24 hours.

Wiring it into your own systems? The documentation covers the API, file formats, webhooks, and moving over from an existing batch setup.

Ready-made jobs

Common workloads

Priced by the outcome you want, so you never have to pick a model first.

responses
24h

Agent Main Thread

Durable, webhook-first single-request inference for patient agent loops.

Delivered in
24h
Output
Checked against your schema
responses
24h

Batch Summarization

Condense long text, reports, and backlogs into concise summaries with next steps.

Delivered in
24h
Output
Checked against your schema
responses
24h

Structured Extraction

Pull fields, entities, and missing data out of messy documents or messages.

Delivered in
24h
Output
Checked against your schema

Find out what you would actually save

Price your real volume in under a minute. No account, no card, no call.