5 depositions of 3 hours: 15 hours of transcript, priced

Transcribing five 3-hour depositions is 15 hours of audio, which costs $8.10 on MAI-Transcribe-2-Streaming and $9.00 on Sume STT 1.0 in 90 ten-minute jobs.

A machine transcript is a working draft for this kind of material, not the record.

The arithmetic

MAI-Transcribe-2-Streaming is priced per hour, Sume STT 1.0 per audio minute, so the sums convert minutes to hours first. The Sume rate is the public rate on the Sume docs; confirm the live number in GET /v1/catalog.

Per minute, MAI works out to $0.009 ($0.54 / 60) against Sume's $0.01, so Sume is about 11 percent higher. On this run the difference is $0.90. Microsoft's rate is an introductory price that its page says runs through the end of the year, and the page does not say what the price becomes afterward, so a budget past December should not assume it.

Treat the output as a draft

Microsoft ranks MAI-Transcribe-2-Streaming first for accuracy on Artificial Analysis, but on 15 hours of speech even a small error rate means thousands of words to check. Run your own sample through either option and count the corrections.

Neither option replaces a certified reporter when a transcript is evidence.

• Keep the audio file and the job id beside every transcript.
• Sume STT has no speaker diarization controls.

Limits that shape a Sume run

Sume does not list MAI-Transcribe-2-Streaming, so the MAI row is a price reference. Sume STT 1.0 takes a public HTTPS audio_url and returns the transcript with word timings; there is no flag for timings because they always come back. Sentence segments are available when you send segmentation.

• duration_seconds is an optional hint from 1 to 600; leave it out and Sume reserves one minute, so send it for a long file.
• language_code is a hint such as en or ko; leave it out for auto-detect.
• diarize and tag_audio_events are fixed server-side, so do not send them; there are no speaker-label controls.
• The MAI price is an introductory rate that Microsoft says runs through the end of the year.

Running it

Submit with stt_create and an idempotency_key, wait with jobs_wait, then read jobs_result for the text and words. Cut recordings longer than 10 minutes at a pause first; audio detach pulls the track out of a video and Timeline audio split slices a Sume-hosted file into ranges.

The figures are list arithmetic as of 2026-10-08. A one-time check of your own audio is worth more than a benchmark, so run one representative file through each option before you commit a budget.