milliseconds.ai vs Jev from TypeSafe AI: why start with milliseconds

If you are choosing between milliseconds.ai and Jev from TypeSafe AI, we recommend starting with milliseconds. You can sign up today, test every capability for free, and move to production at a lower listed input-token rate.

Both products help software make decisions from text. Our focus is making those decisions straightforward to put into a product: route a message, extract a field, find a source span, or select a tool. You get dedicated APIs for the work, scores where they are useful, and one input-token price across capabilities.

Here are the reasons to start with us. Pricing and access details were checked on September 19, 2026.

Start building today, without a waitlist

milliseconds is open for self-service signup. Create an account, get a test key, and start evaluating with 125 million free input tokens each month. No card is required for testing, and the allowance covers every capability. Create an account, pricing and limits.

TypeSafe’s homepage currently directs new users to Join Waitlist. If you want to test an integration now, milliseconds gives you a direct path from signup to API access. TypeSafe homepage.

When you are ready for production, a credit purchase of at least $10 unlocks production access. Auto top-up is optional. Test and production balances are separate, so you can keep experimenting with test keys while paid workloads use production keys. Billing details.

A lower listed price per input token

milliseconds costs $0.04 per million input tokens: $40 per billion. Jev advertises $42 per billion input tokens, equivalent to $0.042 per million. That makes our published input-token rate approximately 4.8% lower. milliseconds pricing, Jev pricing.

Published input-token rate	milliseconds.ai	Jev / TypeSafe AI
Per million input tokens	$0.04	$0.042
Per billion input tokens	$40	$42

Every milliseconds capability uses the same rate, and output tokens carry no charge. Classification scores, extracted fields, and source spans are all included in that pricing model.

These are USD list rates per billed input token. Providers can count input differently, and workflows can require different numbers of calls. For milliseconds, billing includes the validated request body—text, labels, questions, and schemas—and responses expose usage through x-input-tokens. Compare actual usage when estimating your total bill. Token accounting.

Dedicated APIs for the work your product needs

A support product needs to route messages. An intake form needs to find names and dates. A document workflow needs fields it can validate and store. milliseconds provides a named operation for each step.

What you need	milliseconds capability
Check whether text meets a condition	yes-no
Route text into a category or hierarchy	classify, classify-tree
Score text against a described scale	rate
Find an answer and its location in the source	answer
Fill a supported JSON Schema from text	extract
Find occurrences of named entity types	entities
Check a supplied value against the source	verify

The capability reference covers the request and response shapes. We also provide an OpenAI-compatible interface for structured extraction and tool selection, with documented compatibility limits.

Jev takes a different approach: Choice, Score, and Noul questions over shared state. You can mix question types in one request, which is useful when a workflow asks several bounded questions about the same context. TypeSafe primitives.

For teams building around text classification and extraction, we recommend milliseconds’ task-specific interface. The operation you call maps directly to the feature you are implementing.

Get source spans without building a candidate finder

Suppose a billing message contains a subtotal, a credit, and an amount due. Your application needs the amount due, with enough context for someone to review it.

With milliseconds, answer returns a selected source span and character offsets, or a null result. entities returns matching spans by type. Your interface can highlight the selected text so a reviewer can see where the result came from. Answer, Entities.

TypeSafe’s candidate-extraction cookbook has application code find possible values first, then asks Jev to select among them. That is useful when you already have a candidate set. milliseconds’ dedicated span endpoints remove the need to build that candidate-finding step for these calls. TypeSafe extraction cookbook.

For records, extract fills supported schema fields in one API call. Check the supported schema features before integrating; array-of-objects properties currently return empty arrays. Extraction reference.

Put us to the test on your own workload

The best next step is a small evaluation with real examples. Pick one recurring decision, define the expected result, and include ambiguous inputs and missing information. Check the errors, review rate, response time, and cost of the complete workflow.

Classification returns the winning label and a score distribution, so you can inspect competing options and tune an accept-or-review policy. Scores help you make that policy explicit; they are not measured accuracy. Use the threshold-tuning guide to choose settings from labeled data.

We recommend milliseconds for its immediate access, lower listed token rate, and dedicated decision and extraction APIs. Quality and end-to-end performance still depend on your workload; this article does not claim a benchmark win over Jev.

Start with milliseconds today. Create your account, use your free monthly test allowance, and build one decision into your application. The quickstart takes you through the first call.

For the engineering rationale, read why we built decision-machine-1. If you are also considering a general-purpose model, see milliseconds vs LLM APIs.