Updated
Transcription models turn recorded audio into text. For prerecorded files, the real trade-off is accuracy against speed, price, and features like speaker labels - and today’s leaders score so close that the wrong pick is easy to make. We ranked 15 on a shared accuracy benchmark.
Best Transcription Models
#ModelBest for
How to Choose
When choosing between these models, weigh four things:- Access: Decide first whether you’ll use a hosted API, a first-party app, or run the model yourself, because that choice drives cost, privacy, latency, and setup work more than any single benchmark. Only Voxtral Small, Parakeet TDT 0.6B V3, and Whisper Large v3 Turbo are realistic self-host options; the rest are hosted.
- Quality: The score is a 0-100 index built from Artificial Analysis’s AA-WER v2 benchmark, which blends conversational, parliamentary, and earnings-call English audio and rewards lower word error. Treat it as an English-accuracy proxy - it doesn’t fully capture multilingual breadth, diarization, timestamps, noisy telephony, or long-file reliability.
- Price: We use current US dollars per hour of prerecorded audio for the scored route. Token-billed models like Gemini 3.1 Pro and Qwen3.5-Omni-Plus are converted to a comparable hourly figure, and add-ons like diarization or entity detection can push real cost above the base rate.
- Speed Factor: How many seconds of audio each model transcribes per second of processing. If you’re clearing large batches, this matters as much as price - Parakeet TDT 0.6B V3 and Deepgram Nova-3 are in a different league from Gemini 3.1 Pro.
Other Models We Considered
Frequently Asked Questions
What is the best transcription model right now?
What is the best transcription model right now?
For most mixed-content work, Scribe v2 is our top overall pick - near-leading accuracy with the diarization, timestamps, and language coverage real transcripts need. MAI-Transcribe-1.5 and Pulse Pro match it on raw accuracy and are much faster, so consider them when throughput matters - just note MAI’s preview status and Pulse Pro’s English-only limit.
What is the best transcription model for most people?
What is the best transcription model for most people?
If you want one safe default, Scribe v2. If your audio is English and you care about cost and speed, Pulse Pro or a hosted Whisper Large v3 Turbo will do the job for less. Match the model to your audio rather than chasing the top score.
What is the best open-weight transcription model you can run locally?
What is the best open-weight transcription model you can run locally?
For a typical laptop, Parakeet TDT 0.6B V3 is the fastest and lightest, and Whisper Large v3 Turbo is the easiest with the biggest ecosystem. Voxtral Small scores higher and is genuinely multilingual, but its 24B weights need a high-end GPU or heavy quantization.
What is the cheapest transcription model that's still good?
What is the cheapest transcription model that's still good?
Hosted, Whisper Large v3 Turbo and Parakeet TDT 0.6B V3 are the cheapest per hour, and Soniox v5 Async bundles diarization and translation into a very low rate. Self-hosting Parakeet or Whisper drops the cost to just your own compute.
Which transcription model is best for multiple languages?
Which transcription model is best for multiple languages?
Soniox v5 Async and Speechmatics Enhanced cover broad language sets with diarization built in, and Scribe v2 spans many languages with rich output. For noisy European business calls specifically, Solaria-3 is tuned for that; for the widest coverage, Gladia’s older Solaria-1 still leads.
Which is best for meetings and speaker labels?
Which is best for meetings and speaker labels?
You need diarization. Scribe v2, Universal-3.5 Pro, and Voxtral Mini Transcribe 2 all handle it well. Avoid MAI-Transcribe-1.5 here - it’s fast and accurate but has no speaker diarization.
Do transcription benchmarks match real-world use?
Do transcription benchmarks match real-world use?
Partly. Our score is English-only and rewards low word error on conversational, parliamentary, and earnings audio. It won’t tell you how a model handles your languages, accents, background noise, overlapping speakers, or long files - test a shortlist on your own audio before committing.