Choose Your ASR (Speech-to-Text) Model: One Platform, Every Top Model
The problem with single-model transcription tools
Most transcription tools lock you into one ASR (speech-to-text) provider. When a better model comes out — or you realize a different model handles your language, speaker count, or audio quality better — you have to switch platforms entirely. That means new accounts, different export formats, and rebuilding your workflow from scratch.
ASR (speech-to-text) models are evolving fast. In the past year alone, we've seen GPT-4o Transcribe Diarize launch with built-in speaker identification, Qwen3-ASR-Flash take #1 on the HuggingFace Open ASR Leaderboard, and ElevenLabs Scribe v2 top the Artificial Analysis rankings at 2.3% WER.
No single model is best for every use case. That's why we built Transcribe.so to let you choose.
How model selection works on Transcribe.so
When you create a transcription, you pick your ASR (speech-to-text) model from a dropdown. Everything else — the AI analysis pipeline, the interface, the export options — stays exactly the same regardless of which model you choose.
Currently available:
- GPT-4o Transcribe Diarize — best for multi-speaker content. Built-in speaker identification, 57 languages, segment-level timestamps.
- Qwen3-ASR-Flash — best for accuracy and subtitles. #1 on HuggingFace Open ASR Leaderboard (4.25% WER), 33 languages plus 22 Chinese dialects, word-level timestamps, emotion detection.
- Voxtral Mini Transcribe — word-level timestamps + speaker diarization across 40 languages. Context biasing for proper nouns. Lowest cost per minute.
Coming soon:
- ElevenLabs Scribe v2 — 2.3% WER, 99 languages, speaker diarization
- Google Gemini — multimodal audio processing, 100+ languages
- Amazon Transcribe — enterprise-grade with HIPAA eligibility and custom vocabulary
Same workflow, any model
Regardless of which ASR (speech-to-text) model you choose, every transcription gets:
- Chapters and sections — auto-generated navigable structure
- Speaker identification — with GPT-4o Transcribe Diarize
- Semantic search — find moments by meaning, not just keywords
- AI Q&A with citations — ask questions, get answers with exact timestamps and YouTube playback links
- AI summaries and takeaways — key points with speaker attribution
- Subtitle export — SRT, WebVTT, karaoke VTT, JSON with platform presets for YouTube, TikTok/Shorts, Netflix-style, Podcast, and Broadcast
- Markdown export — chapters, sections, search results, Q&A history with YouTube timestamp links for Notion, Obsidian, and other tools
The ASR (speech-to-text) model handles step one — turning audio into text. Everything after that is the same pipeline.
When to choose each model
| Use case | Model | Why |
|---|---|---|
| Podcasts, interviews, meetings | GPT-4o Transcribe Diarize | Built-in speaker labels |
| Maximum accuracy, single speaker | Qwen3-ASR-Flash | Lowest WER on Open ASR Leaderboard |
| Subtitle generation | Qwen3-ASR-Flash | Word-level timestamps for precise cue boundaries |
| Chinese dialects | Qwen3-ASR-Flash | 22 dialect support |
| Long-form audio (3+ hours) | Qwen3-ASR-Flash | 12-hour native, no chunking |
| Budget-conscious | Qwen3-ASR-Flash | ~$2/hr vs ~$4/hr |
For detailed benchmarks and pricing for every model, see the complete ASR (speech-to-text) model guide.
Why this matters for creators, podcasters, editors, and learners
Whether you're a creator producing YouTube videos, a podcaster publishing episodes, an editor cutting footage, or a curious learner studying lectures — you need different things at different times:
- Speaker labels for interview clips → GPT-4o Transcribe Diarize
- Word-level subtitles for TikTok captions → Qwen3-ASR-Flash
- Budget-friendly bulk transcription for your back catalog → Qwen3-ASR-Flash
With Transcribe.so, you don't need separate tools for each. Choose the model, get your transcript, export SRT or WebVTT subtitles directly into CapCut, Premiere Pro, DaVinci Resolve, or Final Cut Pro.
Try it
Upload a YouTube link or audio file at transcribe.so, choose your model, and see the full pipeline in action.
The same model picker is wired into the Transcribe.so Custom GPT in ChatGPT, the Claude Custom Connector, and the public Bearer-auth API. One pipeline, four surfaces.