The Most Accurate Transcribing Tool for Any Language — Chapters, Q&A, and Export-Ready Subtitles
Why accuracy depends on the model — and the language
No single speech-to-text model is the most accurate for every language. English accuracy can differ by 1–2% WER between models, but for languages like Arabic, Hindi, or Hungarian the gap widens to 5–10%. Choosing the wrong model means more cleanup, more re-listening, and more wasted time.
Transcribe.so solves this by giving you access to multiple world-class ASR models on one platform — so you can pick the one that scores best for your language, based on published benchmarks.
The models we support
GPT-4o Transcribe Diarize
Provider: OpenAI Languages: 57 | Best for: Multi-speaker content with speaker identification
OpenAI's premium model with built-in speaker diarization. If your audio has multiple speakers — podcasts, meetings, interviews — this is the model that labels who said what.
- OpenAI official announcement
- Artificial Analysis independent benchmark — AA-WER: 4.1%
- Voice Writer real-world leaderboard — mean WER: 5.4% across clean, noisy, accented, and specialist speech
Published FLEURS WER (lower = better):
| Language | GPT-4o WER |
|---|---|
| English | 2.40% |
| Chinese (Mandarin) | 2.44% |
| Cantonese | 4.98% |
OpenAI claims broad multilingual WER gains on FLEURS, but a detailed per-language breakdown is not yet public. The three values above come from the Qwen3-ASR technical report (Table 3), which tested GPT-4o Transcribe against its own model on the same benchmark.
Qwen3-ASR-Flash
Provider: Alibaba Qwen Languages: 33 + 22 Chinese dialects | Best for: Maximum accuracy, word-level timestamps, long-form audio
Ranked #4 of 80+ on the HuggingFace Open ASR Leaderboard with a 6.37% average WER across 9 test sets — nearly 2× better than Whisper-large-v3.
- Qwen3-ASR technical report — full per-language WER tables on FLEURS, MLS, Common Voice, and MLC-SLM
- Qwen3-ASR-1.7B model card — benchmark tables and GPT-4o comparison
- Open ASR Leaderboard paper — 86 systems compared across 12 datasets
Published FLEURS WER for 29 languages:
| Language | WER | Language | WER | |
|---|---|---|---|---|
| Italian | 1.60% | Korean | 2.07% | |
| Chinese (Mandarin) | 2.38% | Spanish | 2.68% | |
| English | 2.72% | German | 3.03% | |
| Japanese | 3.09% | Portuguese | 3.18% | |
| French | 3.44% | Cantonese | 3.50% | |
| Indonesian | 3.65% | Vietnamese | 3.64% | |
| Dutch | 4.35% | Russian | 4.81% | |
| Thai | 5.53% | Turkish | 6.13% | |
| Polish | 7.24% | Romanian | 10.45% | |
| Malay | 11.37% | Danish | 11.85% | |
| Finnish | 12.21% | Hindi | 13.77% | |
| Greek | 13.85% | Arabic | 14.78% | |
| Swedish | 15.02% | Filipino | 19.17% | |
| Persian | 18.37% | Czech | 18.68% | |
| Hungarian | 21.77% |
Source: Qwen3-ASR technical report, Table A.2(b)
Voxtral Mini Transcribe
Provider: Mistral AI Languages: 13 | Best for: Word-level timestamps, subtitle generation, lowest cost per minute
Mistral's dedicated transcription model with word-level timestamps, speaker diarization, and context biasing (up to 100 custom terms).
- Voxtral technical paper — per-language WER on FLEURS, Common Voice, and MLS
- Voxtral launch post — benchmark summary and language list
- Artificial Analysis — AA-WER: 3.7% (Mini), 2.9% (Small)
Published FLEURS WER for 9 languages:
| Language | WER |
|---|---|
| Italian | 2.31% |
| Spanish | 2.75% |
| German | 3.54% |
| Portuguese | 3.57% |
| English | 3.61% |
| French | 4.22% |
| Dutch | 4.89% |
| Hindi | 10.32% |
| Arabic | 14.64% |
Source: Voxtral paper, Table 4
Head-to-head: WER by language on FLEURS
Where two or more models have published benchmarks on the same language, here's how they compare. Bold = best score for that language.
| Language | Qwen3-ASR-Flash | Voxtral Mini | GPT-4o Transcribe |
|---|---|---|---|
| Italian | 1.60% | 2.31% | — |
| Korean | 2.07% | — | — |
| Chinese (Mandarin) | 2.38% | — | 2.44% |
| English | 2.72% | 3.61% | 2.40% |
| Spanish | 2.68% | 2.75% | — |
| German | 3.03% | 3.54% | — |
| Portuguese | 3.18% | 3.57% | — |
| French | 3.44% | 4.22% | — |
| Cantonese | 3.50% | — | 4.98% |
| Dutch | 4.35% | 4.89% | — |
| Hindi | — | 10.32% | — |
| Arabic | — | 14.64% | — |
"—" means no published FLEURS WER for that model. Hindi and Arabic only have Voxtral and Qwen benchmarks; for those, Qwen scores 13.77% (Hindi) and 14.78% (Arabic) on FLEURS — close to Voxtral's numbers.
Key takeaway: Qwen3-ASR-Flash leads on most languages. GPT-4o wins on English (2.40% vs 2.72%). Voxtral competes well on Romance languages (Italian, Spanish, Portuguese). The "best" model depends on your language.
More than a transcript: what you get on every transcription
Choosing the right model is step one. Everything after the transcription is the same AI pipeline, regardless of which model you pick:
Chapters
Long audio automatically broken into titled, summarized chapters. A 2-hour podcast becomes a structured outline you can scan in 30 seconds. Learn more about chapter generation →
AI Q&A with citations
Ask any question about your transcript and get an answer with exact timestamps. "What did the guest say about pricing?" → answer + clickable timestamp. No more scrubbing through 90 minutes of audio.
Semantic search
Find any moment across your entire transcript library using natural language. Frontier embeddings let you find "the part about budget cuts" even if those exact words were never spoken.
Subtitle export
Export SRT, WebVTT, karaoke VTT (word-by-word highlighting), or JSON. Platform presets for YouTube, TikTok/Shorts, Netflix-style, Podcast, and Broadcast. Import directly into CapCut, Premiere Pro, DaVinci Resolve, and Final Cut Pro — no timing fixes needed. See the subtitle export guide →
Speaker identification
GPT-4o Transcribe Diarize and Voxtral Mini both provide automatic speaker labels. Know who said what without manual tagging.
Sections and summaries
Every transcription gets AI-extracted sections, keywords, key quotes, and a structured summary. Turn hours of content into scannable insight.
How pricing works
Pricing is flat, not per-seat and not per-minute. Transcription on our self-hosted engine is unlimited on every paid plan. Premium models (GPT-4o, Voxtral) are pay-as-you-go from your wallet.
| Plan | Price | What you get |
|---|---|---|
| Free | $0 | 5 hours/mo of transcription |
| Pro | $19/mo | Unlimited transcription |
| Business | $49/mo | Unlimited transcription + $10/mo premium credit |
| Enterprise | Custom | Volume pricing, SSO, support SLAs |
Annual billing gives you 2 months free. Every new account starts with a $5 signup credit to test transcript quality in your language. Premium-model usage (GPT-4o, Voxtral) draws from your wallet at pay-as-you-go rates.
Independent benchmarks and leaderboards
We track multiple independent sources to evaluate model quality:
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HuggingFace Open ASR Leaderboard — 9 test sets, community-driven. Qwen3-ASR-Flash ranks #4 of 80+ at 6.37% average WER. Methodology paper.
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Artificial Analysis — Speech-to-Text — Independent AA-WER v2.0 benchmark (AA-AgentTalk 50%, VoxPopuli-Cleaned-AA 25%, Earnings22-Cleaned-AA 25%). Includes speed and pricing comparisons.
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Voice Writer — Real-World STT Leaderboard — English-focused, tests across clean, noisy, accented, and specialist speech categories.
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FLEURS benchmark — Multilingual evaluation. The per-language WER tables throughout this article use FLEURS scores from the Qwen3-ASR and Voxtral technical reports.
Different methodologies, different test sets, different rankings. We reference all of them so you can make an informed choice.
67 languages supported across all models
Between GPT-4o, Qwen3-ASR-Flash (33 plus 22 Chinese dialects), and Voxtral Mini, Transcribe.so covers 67 unique languages. For most languages, at least one model has a published WER benchmark. For the rest, models are supported but no public benchmark exists yet.
See the full per-language breakdown on our ASR model guide.
Related
- Every ASR Model on Transcribe.so: Benchmarks, Pricing, and When to Use Each — full model specs and selection guide
- Choose Your ASR Model: One Platform, Every Top Speech-to-Text Model — why model choice matters
- AI Transcription for Content Creators — subtitle and chapter workflows
- How to Import AI Subtitles into CapCut, Premiere Pro, DaVinci Resolve & Final Cut Pro
Try it
Choose your model at transcribe.so. Upload a file or paste a YouTube URL, pick your pipeline, and get chapters, Q&A, and export-ready subtitles in minutes. Start for $2, no subscription.
The same model picker is exposed in the Transcribe.so ChatGPT Custom GPT and the Claude Custom Connector. Paste a YouTube link to either AI and the lowest-WER model for your language is picked automatically.