# Rev AI vs Whisper (OpenAI)

Rev AI records 50,708 monthly visits with -18.4% month-over-month growth and Free Trial, Paid pricing. Whisper (OpenAI) records 0 monthly visits with — growth and Free pricing.

| Metric | [Rev AI](https://openaitools.app/tool/rev-ai) | [Whisper (OpenAI)](https://openaitools.app/tool/whisper-github) |
| :--- | :--- | :--- |
| Website URL | https://rev.ai | https://github.com/openai/whisper |
| Categories | AI Speech-to-Text, AI Speech Recognition, AI API, AI Transcription | AI Speech Recognition, AI Speech-to-Text, AI Transcriber, AI Translate |
| Pricing Model | Free Trial, Paid | Free |
| Monthly Visits | 50,708 | 0 |
| MoM Growth Rate | -18.4% | — |
| Primary Market | United States | Global |
| Direct Visits | 22,437 | 0 |
| Review Score | 4.4 | 4.8 |
| Saved Count | 1 | 3 |
| Supported Platforms | Website | Open Source |

## Rev AI

Rev AI is the developer-facing platform of Rev.com, focused on delivering speech recognition as an API rather than as a consumer transcription app. It exposes two main speech-to-text paths: an asynchronous endpoint for pre-recorded files and a streaming endpoint for live audio, so teams can build anything from call-center analytics to real-time captioning. Transcripts come back with punctuation, casing, formatting, and word-level timestamps across more than 57 languages.

Beyond raw transcription, Rev AI bundles an "Insights" layer that runs natural-language processing on top of your audio: language identification, sentiment analysis, topic extraction, summarization, and translation. There is also a forced-alignment product that produces precise word-level timing, which is useful for captioning, karaoke-style media apps, search indexing, and accessibility features. The company advertises the lowest word error rate (WER) among major providers and publishes benchmark results, with models trained on a large human-verified speech corpus.

Because it is an API product, the typical customer is an engineering team embedding speech into an existing product. Rev AI emphasizes compliance and scale, citing SOC 2, HIPAA, GDPR, and PCI alignment, encryption in transit and at rest, global deployment options, and 99.99% uptime targets. Pricing is metered per minute of audio processed, with separate rates for the standard engine, foreign-language and Whisper models, and each add-on insight, plus volume-based enterprise contracts.

**Pricing & Plans:** Pay-as-you-go metered pricing per minute of audio, with roughly 5 hours of free credits to start. Published rates include standard English transcription around $0.20/hour, foreign-language transcription around $0.30/hour, Whisper Large at about $0.005/minute, and add-ons like forced alignment and translation priced per minute of audio. Enterprise plans offer volume pricing, dedicated support, and additional security controls.

- **Async and streaming speech-to-text:** Transcribe pre-recorded files in minutes or stream live audio through a real-time endpoint, with punctuation, casing, and formatting applied automatically.
- **57+ languages with low word error rate:** Recognize speech across more than 57 languages and dialects, with benchmarked accuracy that the company reports as the best among major vendors.
- **Audio Insights (NLP add-ons):** Layer language identification, sentiment analysis, topic extraction, summarization, and translation on top of any transcript to turn audio into structured intelligence.
- **Forced alignment and precise timestamps:** Generate exact word-level timing for media, subtitles, accessibility, and content indexing workflows.
- **Enterprise-grade security and global deployment:** SOC 2, HIPAA, GDPR, and PCI compliant options, encryption in transit and at rest, cloud or on-prem deployment, and 99.99% uptime targets.

**Strengths:** Very accurate ASR with published benchmark comparisons and strong performance across accents and ethnic backgrounds; Broad add-on NLP toolkit (sentiment, topics, summarization, translation) available from the same API key; Backed by Rev.com's established transcription business, with HIPAA, SOC 2, and GDPR coverage; Pay-as-you-go meters let small teams start cheaply, and enterprise volume terms are available

**Limitations:** Per-minute metering across several add-on products can be hard to forecast at high volume; No consumer-friendly editing interface on the API platform, so end users need their own frontend; Human transcription, the company's flagship consumer offering, is billed separately and far more expensively

[Profile (Markdown)](https://openaitools.app/tool/rev-ai.md)

## Whisper (OpenAI)

Whisper was introduced by OpenAI as a general-purpose speech recognition model trained on a very large, diverse dataset of web audio. Instead of stacking separate acoustic, language and punctuation modules, it treats every speech task as a token prediction problem: transcription, translation, language identification and voice activity detection all share one Transformer encoder-decoder and are distinguished by special task tokens. That single-model design is why a straightforward `pip install` is enough to get multilingual transcription running on a laptop, and why Whisper has become a de facto baseline for open speech-to-text work.

The repository ships six model sizes — tiny, base, small, medium, large and a faster turbo variant — trading accuracy for memory and speed. Each size also has an English-only `.en` counterpart that tends to score better on English audio, while the multilingual checkpoints are the ones to use when translating non-English speech into English. Practical VRAM needs range from roughly 1 GB for tiny up to about 10 GB for large, so the smaller checkpoints run comfortably on consumer hardware or even CPU, while large models are usually paired with a GPU.

Because Whisper is a model and a Python package rather than a hosted product, there is no dashboard, account or quota. You install it, point the command line at audio files, and write your own integration on top. It supports many audio formats through ffmpeg, and its per-language accuracy varies widely — strong for high-resource languages, weaker for low-resource ones — which is worth knowing before adopting it for production transcription.

**Pricing & Plans:** Free and open source. The code and weights are published on GitHub under the MIT license with no usage fees or per-minute charges, though you pay for whatever compute you run it on and any OpenAI API usage if you pair it with their hosted services.

- **Multitask single model:** One Transformer handles transcription, speech translation into English, language identification and voice activity detection, selected through special task tokens.
- **Multiple model sizes:** Choose from tiny, base, small, medium, large and turbo checkpoints, with English-only .en variants, to balance accuracy against VRAM and speed.
- **Broad language coverage:** Multilingual checkpoints transcribe and translate dozens of languages, with automatic language detection on mixed-language audio.
- **Command-line and Python API:** Install via pip with openai-whisper and transcribe files from the terminal or from your own Python code, with ffmpeg handling audio decoding.
- **Permissive MIT license:** Freely embed, modify and redistribute the code and weights in commercial and personal projects without licensing fees.

**Strengths:** Completely free and open source under a permissive MIT license; Strong multilingual accuracy across many languages with automatic detection; Runs locally, so audio never has to leave your hardware; Several model sizes let you match accuracy to available compute

**Limitations:** No hosted UI, account or support — you must install and operate it yourself; Large models need substantial VRAM and a GPU for reasonable speed; Accuracy drops noticeably on low-resource languages and noisy audio; Setup requires Python, PyTorch and ffmpeg, which can be fiddly for non-developers

[Profile (Markdown)](https://openaitools.app/tool/whisper-github.md)

## Frequently Asked Questions

### How do Rev AI and Whisper (OpenAI) compare in monthly traffic?

Rev AI has 50,708 monthly visits, while Whisper (OpenAI) has 0. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Rev AI has a month-over-month growth rate of -18.4%; Whisper (OpenAI) has —.

### How does pricing compare?

Rev AI: Free Trial, Paid. Pay-as-you-go metered pricing per minute of audio, with roughly 5 hours of free credits to start. Published rates include standard English transcription around $0.20/hour, foreign-language transcription around $0.30/hour, Whisper Large at about $0.005/minute, and add-ons like forced alignment and translation priced per minute of audio. Enterprise plans offer volume pricing, dedicated support, and additional security controls. Whisper (OpenAI): Free. Free and open source. The code and weights are published on GitHub under the MIT license with no usage fees or per-minute charges, though you pay for whatever compute you run it on and any OpenAI API usage if you pair it with their hosted services.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Fireflies.ai](https://openaitools.app/tool/fireflies-ai) | 4,411,502 | +3% | Freemium | AI teammate that records, transcribes and summarizes every meeting | [.md](https://openaitools.app/tool/fireflies-ai.md) |
| 2 | [Happy Scribe](https://openaitools.app/tool/happyscribe-com) | 4,088,330 | +8% | Freemium, Free Trial, Paid | AI transcription and meeting notes in 150+ languages | [.md](https://openaitools.app/tool/happyscribe-com.md) |
| 3 | [Rev](https://openaitools.app/tool/rev) | 1,524,237 | -12.9% | Contact for Pricing | AI and human transcription, captions and investigative case analysis in one platform | [.md](https://openaitools.app/tool/rev.md) |
| 4 | [Video Transcriber AI](https://openaitools.app/tool/video-transcriber-ai) | 1,320,064 | +11% | Freemium | Turn any video or audio into text in seconds | [.md](https://openaitools.app/tool/video-transcriber-ai.md) |
| 5 | [Deepgram Voice AI](https://openaitools.app/tool/deepgram-voice-ai) | 863,852 | +31.8% | Free Trial | Real-time speech-to-text, text-to-speech and voice agents in one API platform | [.md](https://openaitools.app/tool/deepgram-voice-ai.md) |
| 6 | [Sonix AI](https://openaitools.app/tool/sonix-ai) | 646,532 | +8% | Free Trial, Paid, Contact for Pricing | AI transcription, translation and subtitles for audio and video | [.md](https://openaitools.app/tool/sonix-ai.md) |
| 7 | [Transkriptor](https://openaitools.app/tool/transkriptor-com) | 628,671 | -3.6% | Free Trial, Paid | AI transcription that turns audio and video into searchable text | [.md](https://openaitools.app/tool/transkriptor-com.md) |
| 8 | [AssemblyAI](https://openaitools.app/tool/assemblyai) | 453,591 | -6.9% | Freemium, Contact for Pricing | Speech-to-text and voice intelligence APIs for developers | [.md](https://openaitools.app/tool/assemblyai.md) |
| 9 | [Tongyi Tingwu](https://openaitools.app/tool/tongyi-tingwu) | 425,484 | -10% | Free | Your AI assistant for work and study. | [.md](https://openaitools.app/tool/tongyi-tingwu.md) |
| 10 | [Soniox](https://openaitools.app/tool/soniox) | 335,647 | +2.2% | Paid | One speech AI API for recognition, generation, and translation in 60+ languages | [.md](https://openaitools.app/tool/soniox.md) |

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Source: https://openaitools.app/compare/rev-ai-vs-whisper-github

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