Whisper is OpenAI's open-source speech recognition model, released in 2022 and now (2026) the dominant choice for English transcription and a strong option for 90+ other languages. Whisper is open-weight (MIT licensed), runs on consumer hardware in its smaller variants, and powers a huge fraction of the AI transcription market — from podcast tools to meeting recorders to call-centre analytics.
Whisper's variants in 2026:
- Whisper Large v3 / v4 — the latest open-source flagships; highest accuracy across languages.
- Whisper Turbo — distilled variant from OpenAI; ~8x faster than Large v3 with minor quality loss.
- Distil-Whisper — community distillations for cheap CPU inference.
- Whisper API (OpenAI) — hosted Whisper via OpenAI's API; convenient but not the cheapest option at high volume.
- Groq Whisper — extreme-low-latency hosted Whisper for real-time use cases; under 200ms time-to-first-word.
Capabilities and use cases:
- Transcription — convert speech to text; the core capability.
- Translation — translate speech to English in one step (translation to other languages requires a separate step).
- Word-level timestamps — for subtitles, captions and search inside long recordings.
- Voice activity detection — find when speech is happening in long audio.
- Diarisation (with help from companion models) — who said what.
Production deployments commonly use Whisper for:
- Podcast and interview transcription — Descript, Riverside, Otter.ai all use Whisper or comparable models.
- Meeting recording — Granola, Fathom, tldv, Krisp, Zoom AI Companion.
- Call centre analytics — transcription plus downstream sentiment and topic analysis.
- Subtitling and captioning — YouTube, Vimeo, broadcast systems.
- Voice notes in apps — voice-to-text inputs in productivity tools.
- Voice agents — paired with an LLM and a TTS model for conversational AI.
The competitive landscape:
- Whisper / Whisper-derived — dominant in open-source and most independent products.
- Deepgram, AssemblyAI, Rev.ai — commercial APIs with proprietary models; often beat open Whisper on specific use cases (high accent diversity, specialised vocabularies).
- Google Speech-to-Text, Azure Speech, AWS Transcribe — cloud-native options popular in enterprise.
- Apple's on-device speech recognition — strong for iOS-native apps with privacy requirements.
For a US team building voice features in 2026, Whisper is the default starting point. Self-host the model for cost and privacy; use OpenAI or Groq's hosted Whisper when you want zero ops; consider Deepgram or AssemblyAI when you need specialised features (custom vocabularies, speaker diarisation, real-time low latency at scale). The category has commoditised dramatically — high-quality transcription is now a few cents per hour of audio, and it has moved from "AI feature" to "table stakes" in any product handling spoken content.
