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Introducing decision-machine-1, a language model for making decisions when speed matters.

Try itGet a free API key
Built for application logic.Why a decisions model
The model

It’s like generative AI.
Without the generation.

In software, an AI response often becomes a branch, a field, or a score. The application needs the value to take its next step.

decision-machine-1 scores labels and extracts values directly from text. For these bounded tasks, it skips the token-by-token generation an LLM uses—even with structured outputs.

Read the model rationale
Language in
“Losing sales,
fix this today.”

Is the customer expressing urgency?

Decision outtrue

Probability of urgency 1.00

A recorded /yes-no response. Your code can use the boolean to raise a ticket’s priority.
01

Skip output decoding.

The model reads the input in a forward pass. It does not spend additional decoding steps writing an answer token by token.

02

Define the task.

Supply labels to choose between, questions to answer from the source, or fields to extract.

03

See the alternatives.

A classification includes the full score distribution and confidence. Your code can see when two labels compete, instead of treating every winner alike.

Calls

One model.
Seven decisions.

Choose the labels, questions, or fields your application needs.

POST /classifyRecorded API example · edit and run it live
Request

Choose a destination from your support queues.

Label
billing

Use the label to select the billing queue.

Label probability0.74
billing0.74
other0.25
technical0.008
shipping0.001
View JSON response 
{
  "label": "billing",
  "probability": 0.74,
  "confidence": 0.555,
  "scores": {
    "billing": 0.74,
    "shipping": 0.001,
    "technical": 0.008,
    "other": 0.25
  }
}
Recorded examples · 16 September 2026Get a free API keyAPI quickstart
Support triageA label selects the queue. A score sets priority. A boolean flags urgency. Document intakeExtract fields, check values against the source, then validate before writing a record.
A place beside your LLM

Call your LLM
if you need to.

Use a decision call first, then accept results that meet your thresholds, and send ambiguous cases to an LLM or a person.

That can reduce large-model calls. It also adds a first step, so measure the cost and latency of the whole path on your traffic.

Build an LLM cascade
Recorded classificationbilling
Label probability 0.74Confidence 0.555
Your application’s gateprobability >= 0.90
confidence >= 0.70
Meets both thresholdsUse the result
Below either · this exampleLLM or human review
Illustrative thresholds, not accuracy guarantees. Tune on your own examples; your application implements the routing.
03 — PricingBuilt for volume.

Small decisions.
At large scale.

Screen incoming messages. Check extracted fields. Cost follows the text you send, with no output-token charge.

Get a free API key
$0.04

per million input tokens

Output tokens$0
Free input tokens per month125M

The free plan includes 125 million input tokens each month. After that, 100 million input tokens cost $4. Each call bills its input, including repeated text.

04 — IntegrationDesigned for your stack.

Start with
a real task.

Try a classification or extraction call you already send to an LLM. Compare accuracy, latency, and cost on representative inputs.

Use plain HTTP, or reuse your OpenAI client for supported structured-output and tool-call requests.

Create a free account

Read the quickstart · Explore the documentation

Your first request
curl https://api.milliseconds.ai/v1/decision-machine-1/yes-no \
  -H "Authorization: Bearer $MILLISECONDS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"text":"Losing sales, fix this today.",
       "statement":"The customer expresses urgency."}'
Recorded response{ "answer": true, "probability": 1 }

Keys come from the developer console. The free plan includes 125M tokens per month; usage pricing applies after.

Important questions, fast answers.

Does structured output mean the answer is correct?

No. A well-formed response can still contain a wrong value. Evaluate on your own data, validate business rules, and review consequential or ambiguous results. Verification can check a value against the source; it is not a guarantee of correctness.

When should I use an LLM instead?

Use an LLM for writing, conversation, multi-step reasoning, or judgments that need knowledge beyond the supplied text. Use milliseconds when the task has defined labels, questions, or fields.

What do probability and confidence tell me?

For classification, probability is the winning label’s share of the scores. Confidence describes how concentrated the whole distribution is. Neither is a guarantee that the answer is right. Other capabilities return different scoring fields; tune thresholds for the task and the cost of a mistake.

Can I keep my OpenAI SDK?

Yes, for JSON-schema extraction and tool calls. Set the base URL to https://api.milliseconds.ai/v1 and select decision-machine-1. This is not a drop-in chat model: plain chat is unsupported, and the compatibility layer reads user turns only.