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 #1 on the HuggingFace Open ASR Leaderboard with a 4.25% 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. Powered by text-embedding-3-large (3072-dimensional vectors) — 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.
Topics and summaries
Every transcription gets AI-extracted topics, keywords, key quotes, and a structured summary. Turn hours of content into scannable insight.
How pricing works
Every model follows the same structure: pay-per-minute, no subscription lock-in. Subscription tiers reduce the per-minute rate.
| Model | Free tier | Basic ($12/mo) | Plus ($39/mo) | Pro ($99/mo) |
|---|---|---|---|---|
| GPT-4o Diarize | $4.34/hr | $4.04/hr | $3.85/hr | $3.54/hr |
| Qwen3-ASR-Flash | $2.17/hr | $2.02/hr | $1.92/hr | $1.77/hr |
| Voxtral Mini | Lowest | — | — | — |
Start with a one-time $2 payment to test transcript quality in your language. No subscription required.
Independent benchmarks and leaderboards
We track multiple independent sources to evaluate model quality:
-
HuggingFace Open ASR Leaderboard — 9 test sets, community-driven. Qwen3-ASR-Flash is #1 at 4.25% average WER. Methodology paper.
-
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.
-
Voice Writer — Real-World STT Leaderboard — English-focused, tests across clean, noisy, accented, and specialist speech categories.
-
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.
50+ languages supported across all models
Between GPT-4o (57 languages), Qwen3-ASR-Flash (33 + 22 Chinese dialects), and Voxtral Mini (13 languages), Transcribe.so covers 58 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.