2.2% WER · ElevenLabs
SPEECH TO TEXT / JULY 2026
How many words
fall through the cracks?
Compare transcription errors, throughput, API cost, and the open-weight models that can listen locally on your Mac.
00:04.2
The frontier is not one model.
00:08.7
It is the edge between cost speed and accuracy.
00:13.1
1 substitution / 20 words · 5.0% WEREvery dropped word changes the meaning.
2.4% WER · 265.9× realtime
904.9× realtime · open weights
$0.37 / 1,000 minutes
THE ERROR MAP
Fewer mistakes, less friction.
Word error rate ↓ lower is better
2%
3%
4%
5%
6%
$0.25
$0.5
$1
$2
$4
$8
$16
API price per 1,000 audio minutes → log scale
HOSTED MODEL
MAI-Transcribe-1.5
Microsoft
- AA-WER
- 2.4%
- Speed
- 265.9×
- Price
- $6 / 1K min
- Frontier
- No
PRIVATE TRANSCRIPTION
Let your Mac do the listening.
Change memory and precision to plan a local setup. Availability varies by runtime, so treat these as weight-based estimates—not measured throughput on every Apple chip.
Best ranked fitVoxtral Mini
3.8% provider AA-WER
Model / useAA-WERMemoryLanguagesTimestampsLicenseFit
Whisper TinyOpenAI · 39M · The smallest useful baseline; ideal for on-device commands.—not in snapshot1.4 GB99SegmentApache 2.0ComfortableWhisper SmallOpenAI · 244M · A dependable multilingual choice for older Apple silicon.—not in snapshot1.7 GB99SegmentApache 2.0ComfortableParakeet TDT 0.6B v3NVIDIA · 600M · The speed leader; official weights now include MLX support.4.5%provider run2.1 GB25Word + segmentCC BY 4.0ComfortableWhisper Large v3 TurboOpenAI · 809M · Broad language coverage with a much smaller decoder than Large v3.4.6%provider run2.4 GB99Word + segmentMITComfortableWhisper Large v3OpenAI · 1.55B · The compatibility baseline with a mature local tooling ecosystem.4.1%provider run3.2 GB99Word + segmentApache 2.0ComfortableCanary Qwen 2.5BNVIDIA · 3B total · Strong English transcription plus optional transcript post-processing.4.3%provider run4.9 GBEnglishSegmentCC BY 4.0ComfortableVoxtral MiniMistral · 3B · Adds translation and audio understanding beyond plain transcription.3.8%provider run4.9 GB8SegmentApache 2.0ComfortableVoxtral SmallMistral · 24B · Best open-weight AA-WER here, but dramatically heavier than ASR-only models.2.8%provider run30 GB8SegmentApache 2.0Too largeBENCHMARK YOUR AUDIO
WER is the start, not the answer.
Use your domain
Meetings, calls, lectures, and medical dictation have different failure modes. Keep a human transcript set.
Normalize once
Standardize punctuation, casing, numbers, and hesitations before comparing word error rate.
Slice the errors
Break results down by language, accent, noise, speaker overlap, proper nouns, and audio duration.
Time the whole path
Include upload, endpointing, diarization, and post-processing—not just model inference.