ElevenLabs STT 3 GB / 10 hour limit vs Sume STT 600 s jobs
ElevenLabs speech to text accepts files up to 3 GB and 10 hours in standard mode. Sume STT 1.0 reserves up to 10 minutes per job, so cut long recordings.

ElevenLabs' speech-to-text FAQ says files up to 3 GB are supported, and standard mode takes up to 10 hours. Sume STT 1.0 plans around much shorter jobs: the duration_seconds hint is 1 to 600, and the OpenAPI text says to omit it to reserve for 1 minute, with a maximum of 10 minutes. The schema describes that hint as a usage reservation rather than a stated audio-length cap, so cutting a long recording into pieces of 600 seconds or less is the safe plan, not a documented hard limit.
Vendor numbers are from the ElevenLabs page; Sume numbers from the OpenAPI reference and the Audio detach docs, read 2026-10-01.
What are the ElevenLabs limits?
The page lists a 3 GB maximum file size, and a maximum duration of 10 hours in standard mode (use_multi_channel=false). In multi-channel mode the combined duration of all channels must be under 10 hours, and the key facts list says 1 hour. Both audio and video files are accepted.
What are Sume's numbers?
| Limit | ElevenLabs (page) | Sume |
|---|---|---|
| Max file size | 3 GB | Not stated for STT; send a public HTTPS audio_url |
| Duration per request | 10 hours standard | duration_seconds 1 to 600 |
| Default reservation | Billed on audio duration | 1 minute if the hint is omitted |
| Rate | See ElevenLabs pricing | $0.01 per audio minute |
How do I transcribe a long recording on Sume?
Split it into chunks of 600 seconds or less, send one STT job per chunk with its duration_seconds, and add each chunk's start offset to its word timings when you join the results. The audio-detach route can produce mono 16 kHz audio for STT and accepts a range, but its source cap is 1800 seconds, so a multi-hour file has to be cut before it reaches that route. See audio detach for speech-to-text.
Word timings are always returned, so each chunk gives you per-word starts and ends to shift.
Where can this go wrong?
Cut points mid-word can split a word across two jobs, so cut on silence if you can. Keep the chunk list and each chunk's offset with your job ids, and set language_code when you know the language; omit it for auto-detect.
Sources
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