Whisper API
OpenAI's open-source speech-to-text
Overview
Whisper is OpenAI's open-source multilingual speech-to-text model — also available via the OpenAI API. State of the art for many languages when released. Widely used as a backbone for downstream transcription products.
Pricing
Pricing shown for reference only. These figures reflect RECATOOLS research as of 20 May 2026 and may be out of date or incomplete. This is not financial or purchasing advice — always confirm the current price on the provider’s official website before making any decision.
Use cases
What you can produce with Whisper API
- Transcribe audio files in dozens of languages to text via the OpenAI API or by running the open-source model locally.
- Translate non-English speech directly into English text in a single pass.
- Self-host the model with tools like whisper.cpp or faster-whisper to transcribe sensitive audio offline with no per-minute API cost.
- Generate timestamped output (SRT or VTT) to produce subtitles and captions for video content.
- Batch-transcribe large archives of recordings — interviews, lectures, podcasts — with a few lines of Python.
- Fine-tune the open-source model on domain-specific audio to improve accuracy for accents or specialist vocabulary.
- Build Whisper in as the speech-to-text layer of a larger app, such as a meeting-notes tool or voice interface.
ASEAN Perspective
Whisper API in Southeast Asia
ASEAN-region availability and pricing notes coming soon. Drop the editorial team a note via /contact/ if you can supply local context (Singapore/Malaysia/Indonesia/Thailand/Vietnam).
Whisper is OpenAI's automatic speech-recognition model, available both as open-source weights and as a hosted API. It delivers excellent multilingual transcription and translation accuracy across noisy real-world audio, and the open weights mean teams can self-host for privacy or run it cheaply via the API at roughly $0.006/minute. It is a default choice for developers building transcription, captioning and voice pipelines.
It is a model, not a polished product: there is no diarisation, real-time streaming or speaker labelling out of the box, so you build the surrounding workflow yourself. Southeast Asian language coverage is decent for major tongues but weaker on low-resource regional languages. Best for technical teams comfortable with code; non-developers should look at a packaged transcription service instead.
What people say
Whisper remains one of the most consequential releases in speech technology: an open-source multilingual speech-to-text model that became the default backbone for a generation of transcription products. It is still available both as open weights (large-v3, plus the faster turbo variant) and through OpenAI's API. But its position shifted in March 2025 when OpenAI shipped gpt-4o-transcribe and gpt-4o-mini-transcribe as API successors, with benchmarks showing meaningfully lower word error rates than Whisper large-v3 at a similar price. OpenAI has not announced a Whisper deprecation, and the open-source model retains a huge advantage the newer API models cannot match: you can run it yourself, offline, for free.
Developer sentiment splits along that line. Whisper is praised for strong multilingual accuracy, permissive licensing, and a massive ecosystem — faster-whisper, whisper.cpp and countless wrappers make it easy to self-host on modest hardware, and for private or high-volume workloads it is still the pragmatic choice. The complaints are equally well documented: Whisper hallucinates. Researchers and engineers have reported fabricated sentences appearing during silences, with an AP investigation and the Koenecke et al. study documenting invented content — a serious problem for medical, legal or compliance transcription. It also lacks native speaker diarization and real-time streaming, forcing developers to bolt on extra tooling.
As a product entry, Whisper is best understood as infrastructure rather than an end-user app: it has no interface of its own, and most people encounter it inside other transcription tools.
Whisper genuinely fits developers who need self-hosted, multilingual transcription with no per-minute fees, and hobbyists transcribing personal audio locally. Teams wanting maximum accuracy via API should evaluate gpt-4o-transcribe instead, and anyone transcribing high-stakes audio must build in human review because of the hallucination record.
Summary of public user & expert reviews, compiled by RECATOOLS.
About this listing
This entry was compiled from publicly available data including Whisper API's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with Whisper API unless explicitly stated.
Third-party AI tools update their pricing, features, availability, and policies frequently. Information here may be outdated by the time you read this — we make reasonable efforts to keep listings current, but cannot guarantee absolute accuracy.
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