Whisper
← Back to All AI Tools1. Should you try Whisper? Overview
| What it is | Whisper = Transformer ASR trained on 680k hours multilingual audio, chunked 30-sec, outputs text/json/srt/vtt with segment+word timestamps; runs as openai/whisper (PyTorch, MIT) or via OpenAI API whisper-1 at $0.006/min, or as community runtimes. |
| Country / HQ | San Francisco, USA (OpenAI) / open source |
| Best for | Batch transcription of podcasts/lectures/meetings/interviews (post-hoc) |
| Check before buying | Watch out: Live captioning / voice agents needing <300ms first token (use Deepgram/gpt-realtime-whisper) |
| Editor rating | 4.4 / 5 |
2. Features What it does
| Feature | What it does |
|---|---|
| Model family | tiny(39M)/base(74M)/small(244M)/medium(769M)/large-v3(1550M)/large-v3-turbo(809M, ~8× speed) — FP32 reference; FP16/INT8 via ports |
| Languages | 99 languages + translate-to-English; strong on accents/noise/code-switching (trained on web audio, not studio) — Quality uneven on low-resource langs |
| Outputs (self-host) | text / json / verbose_json / srt / vtt / tsv; segment + optional word timestamps (whisper_timestamped) |
| Outputs (API whisper-1) | json/text/srt/verbose_json/vtt + timestamp_granularities[] (word/segment) — Only Whisper-1 keeps SRT/VTT; gpt-4o-transcribe returns json/text only |
| Diarization | None native — bolt on pyannote.audio, or use WhisperX (faster-whisper + pyannote + wav2vec2 forced align) |
| Real-time | Batch-only in reference impl; whisper.cpp / faster-whisper can stream with wrappers; API whisper-1 is not real-time (use gpt-realtime-whisper $0.017/min) |
| Runtimes | openai/whisper (PyTorch) · whisper.cpp (C++/ggml, CPU+Metal, edge) · faster-whisper (CTranslate2, 2–4× faster) · WhisperX (diarization+alignment) · insanely-fast-whisper (FlashAttn2) |
| Hosted API | whisper-1 $0.006/min · gpt-4o-mini-transcribe $0.003/min · gpt-4o-transcribe $0.006/min · gpt-4o-transcribe-diarize $0.006/min (diarized_json) — 25MB/file, 1500s/req cap |
3. Pricing Pricing (2026-10, USD)
| Plan | Price / mo | Key limits & who it fits |
|---|---|---|
| Self-hosted (MIT) | $0 + your GPU/CPU compute | Privacy-first, batch volume, offline/edge |
| OpenAI whisper-1 API | $0.006/min ($0.36/audio-hr) | Existing Whisper integrations, SRT/VTT need |
| OpenAI gpt-4o-mini-transcribe | $0.003/min ($0.18/hr) | Cheapest managed batch, json/text only |
| OpenAI gpt-4o-transcribe | $0.006/min ($0.36/hr) | Better accuracy, json/text only |
| OpenAI gpt-4o-transcribe-diarize | $0.006/min | Speaker labels (non-realtime HTTP) |
| OpenAI gpt-realtime-whisper | $0.017/min ($1.02/hr) | Live captioning / voice agents |
| Third-party hosted (Groq/Replicate/Fireworks/Together) | $0.02–0.10/hr (Whisper weights) | High-throughput batch, no OpenAI lock |
4. Integration Access & integrations
| Access type | Details |
|---|---|
| Access | access_type: open-source-model-plus-api access_display: 💻 openai/whisper (Python/CLI) · 🌐 OpenAI /v1/audio/transcriptions · 🔌 whisper.cpp/faster-whisper/WhisperX runtimes · (no official OpenAI consumer UI) |
5. Reviews & Sources External reviews
| Source | Rating | What it says |
|---|---|---|
| GitHub (openai/whisper) | ~75k stars; reference baseline everyone forks; complaints = slow ref impl, hallucination on silence, no diarization | |
| G2 / tool roundups | Not a SaaS, so no G2 entry; in dev surveys ~4.4/5 as model, dinged as product | |
| AISO / AI Pedia / Dev.to 2026 | “Best offline multilingual baseline, at $0 if you have a GPU; but production STT usually wraps it or picks Deepgram/AssemblyAI for realtime+diarization” | |
| Trustpilot / consumer | N/A — users review Otter/Descript, not Whisper itself |
6. Best for / Not for Fit
| ✔ Suitable | ✘ Not suitable |
|---|---|
| • Batch transcription of podcasts/lectures/meetings/interviews (post-hoc) • Privacy-bound orgs (legal/medical/research) wanting audio to never hit a cloud • Developers building custom STT pipelines, already on OpenAI stack • Low-resource / non-English / code-switching audio • Edge/on-device (whisper.cpp on Apple Silicon/RPi) |
• Live captioning / voice agents needing <300ms first token (use Deepgram/gpt-realtime-whisper) • Multi-speaker meetings needing “who said what” out of the box (needs WhisperX/pyannote) • Non-technical users wanting a record-button app (use Otter/Descript/TurboScribe) • High-volume call-center telephony (Deepgram Nova-3 WER better on phone audio) • Teams wanting summarization/sentiment/PII bundled (AssemblyAI) |
7. Competitors How it compares
| Competitor | How it differs | Best for |
|---|---|---|
| Deepgram Nova-3 | Real-time STT, native diarization, ~300ms | $0.26/min? batch $0.0036/min, stream $0.0056/min |
| AssemblyAI Universal-2/3 | Managed STT + summarization/sentiment/PII | $0.12–0.75/hr, 5h free |
| gpt-4o-transcribe / mini | OpenAI’s higher-accuracy hosted Whisper successor (json/text only) | $0.003–0.006/min |
| Groq Whisper large-v3-turbo | Hosted Whisper, LPU speed | ~$0.02/hr |
| Otter.ai / Descript / TurboScribe | End-user transcription apps | Freemium / $10–20 |
| Google Chirp 3 / AWS Transcribe / Azure Speech | Cloud STT suites | $0.0048–0.024/min |
| whisper.cpp / faster-whisper / WhisperX | Open-source runtimes (not competitors, ecosystem) | $0 |
8. Summary Verdict
| Who should pick it | Batch transcription of podcasts/lectures/meetings/interviews (post-hoc) |
| Who should skip it | Live captioning / voice agents needing <300ms first token (use Deepgram/gpt-realtime-whisper) |