| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | $0.02/min | — |
| Capabilities | 14 decomposed | 11 decomposed |
| Times Matched | 0 | 0 |
Converts pre-recorded audio files (submitted via URL) to text through a job-based asynchronous API that returns speaker-segmented monologues with word-level timestamps. The system processes audio through proprietary models trained on 7M+ hours of human-verified speech data, returning structured JSON with speaker IDs and per-word timing information (ts/end_ts fields). Processing typically completes within ~1 minute for standard files, with results retrievable via polling or webhook callbacks.
Unique: Trained on proprietary 7M+ hour human-verified speech corpus with claimed lowest WER across demographic categories (ethnic background, nationality, gender, accent); implements speaker diarization as first-class output in monologue structure rather than post-processing annotation
vs alternatives: Optimized for conversational and telephony audio with built-in speaker segmentation and demographic bias mitigation, outperforming competitors on WER benchmarks across diverse speaker populations
Processes live audio streams with low-latency transcription output, enabling real-time caption generation and live meeting transcription. Implementation details (streaming protocol, latency guarantees, output format) are mentioned in documentation but not technically specified. Supports continuous audio input with incremental transcript updates.
Unique: Unknown — insufficient technical documentation provided for streaming implementation details, protocol specification, or latency characteristics
vs alternatives: Unknown — insufficient data to compare streaming architecture against alternatives like Google Cloud Speech-to-Text or AWS Transcribe streaming
Provides transcription service with compliance certifications (HIPAA, SOC II, GDPR, PCI DSS) and security features including encryption at rest and in transit. Supports on-premises and cloud deployment options enabling data residency requirements. 99.99% uptime SLA ensures service reliability for regulated industries. Enables secure handling of sensitive audio content (healthcare, financial, legal).
Unique: Offers both cloud and on-premises deployment options with compliance certifications (HIPAA, SOC II, GDPR, PCI DSS) and 99.99% uptime SLA; encryption at rest and in transit with undocumented key management
vs alternatives: On-premises deployment option enables data sovereignty for regulated industries; multi-compliance certification supports diverse regulatory requirements without separate integrations
Integrates with Model Context Protocol (MCP) enabling AI assistants (Cursor, VS Code) to access Rev AI transcription capabilities through standardized protocol. Installable on Cursor and VS Code enabling developers to invoke transcription from within IDE. Specific MCP capabilities and integration details not documented.
Unique: Unknown — insufficient technical documentation on MCP integration, exposed capabilities, or protocol implementation details
vs alternatives: Unknown — no documented details on MCP integration scope, performance, or comparison with direct API usage
Enables direct integration with LLM platforms (ChatGPT, Claude) through 'Copy for LLM' and 'Open in ChatGPT/Claude' options. Allows transcripts to be exported in LLM-compatible format for downstream AI processing, summarization, or analysis. Integration mechanism and export format not documented.
Unique: Unknown — insufficient technical documentation on export format, integration mechanism, or LLM compatibility details
vs alternatives: Unknown — no documented details on export format optimization, token management, or comparison with direct LLM API usage
Implements usage-based pricing model where customers pay for transcription based on consumption (billing unit unknown — likely per-minute or per-request). Free tier available for account signup with limits unknown. Enterprise pricing available via custom negotiation. Pricing details not publicly documented in available materials.
Unique: Unknown — insufficient pricing documentation to assess differentiation vs. competitors
vs alternatives: Unknown — no documented pricing rates, free tier limits, or volume discounts compared to Google Cloud Speech-to-Text, AWS Transcribe, or Azure Speech Services
Allows users to inject domain-specific vocabulary, acronyms, and terminology into the transcription model to improve accuracy for specialized language (medical, legal, technical jargon). Implementation mechanism (vocabulary file format, injection method, model adaptation approach) not documented. Improves WER for domain-specific terms by providing context to the underlying ASR model.
Unique: Unknown — insufficient technical documentation on vocabulary injection mechanism, model adaptation approach, or integration with base ASR model
vs alternatives: Unknown — no documented details on vocabulary management, size limits, or performance characteristics compared to competitors
Generates precise word-level timing information by aligning transcribed text back to the original audio waveform, enabling frame-accurate subtitle generation and video synchronization. Uses forced alignment algorithms to map each word to its exact start/end timestamps in the audio. Output includes ts (start time in seconds) and end_ts (end time in seconds) for every transcribed word element.
Unique: Integrated into core transcript output as ts/end_ts fields on every element, providing automatic word-level timing without separate API call; built on 7M+ hour training corpus enabling robust alignment across diverse audio conditions
vs alternatives: Provides word-level timestamps as standard output rather than optional feature, enabling direct subtitle generation without post-processing alignment step
+6 more capabilities
Transcribes audio in 98 languages to text in the original language using a unified Transformer sequence-to-sequence architecture with a shared AudioEncoder that processes mel spectrograms into language-agnostic embeddings, then a TextDecoder that generates tokens autoregressively. The system handles variable-length audio by padding or trimming to 30-second segments and uses task-specific tokens to signal transcription mode, enabling a single model to handle multiple languages without language-specific branches.
Unique: Uses a single shared AudioEncoder across all 98 languages rather than language-specific encoders, trained on 680,000 hours of diverse internet audio enabling zero-shot cross-lingual transfer. The mel-spectrogram preprocessing pipeline (via log_mel_spectrogram) standardizes variable audio into fixed 30-second segments, allowing the same model weights to handle any language without retraining.
vs alternatives: Outperforms language-specific ASR models on low-resource languages and handles 98 languages in a single model, whereas Google Cloud Speech-to-Text and Azure Speech Services require separate API calls per language and have higher latency due to cloud round-trips.
Translates non-English speech directly to English text by using a task-specific token in the TextDecoder that signals translation mode, bypassing the need for intermediate transcription-then-translation pipelines. The AudioEncoder processes mel spectrograms identically to transcription, but the decoder generates English tokens directly from audio embeddings, reducing latency and error propagation compared to cascaded systems.
Unique: Implements end-to-end speech translation via task-specific decoder tokens rather than cascaded transcription-then-translation, eliminating intermediate text generation and reducing error propagation. The decoder uses a special token prefix to signal translation mode, allowing the same AudioEncoder and TextDecoder weights to handle both transcription and translation without separate model branches.
Whisper CLI scores higher at 58/100 vs Rev AI at 55/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
vs alternatives: Faster and more accurate than cascaded pipelines (Google Translate + Speech-to-Text) because it avoids intermediate transcription errors and reduces round-trip latency; however, less flexible than specialized translation models for domain-specific or style-controlled output.
Exposes two levels of API abstraction: a high-level transcribe() function that handles end-to-end transcription with automatic audio loading, preprocessing, and result formatting, and a low-level decode() function that provides fine-grained control over decoding options (beam width, temperature, language constraints). The high-level API is suitable for simple use cases, while the low-level API enables advanced customization for researchers and developers building complex pipelines.
Unique: Provides dual-level API abstraction with transcribe() for simplicity and decode() for control, allowing users to start with simple code and gradually adopt lower-level APIs as needs become more complex. The high-level API automatically handles audio loading, preprocessing, and result formatting, while the low-level API exposes DecodingOptions for fine-grained control.
vs alternatives: More flexible than single-level APIs (like some cloud services that only expose high-level endpoints) because it supports both simple and advanced use cases; however, requires more learning and boilerplate than opinionated frameworks that make decisions for users.
Detects the spoken language in audio by generating a language token from the AudioEncoder embeddings before decoding text, using the model's multilingual training to recognize acoustic patterns distinctive to each language. The system identifies language during the initial decoding step and can be queried directly via the language identification task token, enabling language detection without full transcription.
Unique: Leverages the shared AudioEncoder's learned acoustic representations across 680,000 hours of multilingual training data to identify language without explicit language classification head — the language token emerges naturally from the decoder's first output token, making detection a byproduct of the transcription architecture rather than a separate classifier.
vs alternatives: Supports 98 languages in a single model with zero-shot capability on low-resource languages, whereas language identification libraries like langdetect or textcat require separate training or pre-built models for each language and cannot handle audio directly.
Converts raw audio files in multiple formats (MP3, WAV, M4A, FLAC, OGG) to mel-spectrogram features via FFmpeg decoding and log-scale mel-frequency filtering, then normalizes variable-length audio to fixed 30-second segments via padding or trimming. The pipeline uses whisper.load_audio() for format-agnostic decoding, whisper.pad_or_trim() for segment normalization, and whisper.log_mel_spectrogram() for feature extraction, enabling the model to process diverse audio sources with consistent preprocessing.
Unique: Integrates FFmpeg as a subprocess for format-agnostic audio decoding rather than using Python-only libraries, enabling support for any FFmpeg-compatible format without maintaining codec-specific parsers. The fixed 30-second segment design allows the model to use a single AudioEncoder without variable-length handling, simplifying the architecture at the cost of preprocessing inflexibility.
vs alternatives: Handles more audio formats than librosa-based pipelines (which require separate codec installations) and avoids the latency of cloud-based audio conversion services; however, less flexible than custom preprocessing pipelines that can adjust segment length or mel-spectrogram parameters.
Generates transcription or translation tokens autoregressively using a TextDecoder that processes AudioEncoder embeddings and previously generated tokens, with support for multiple decoding strategies including greedy decoding, beam search, and temperature-based sampling. The system uses a sliding-window context approach to handle audio longer than 30 seconds by processing overlapping segments and merging results, and supports DecodingOptions for fine-grained control over decoding behavior (beam width, temperature, language constraints).
Unique: Implements sliding-window decoding for long audio by processing overlapping 30-second segments and merging results via token-level overlap detection, avoiding the need to retrain the model for variable-length inputs. The DecodingOptions abstraction allows fine-grained control over beam width, temperature, language constraints, and other decoding parameters without modifying model weights.
vs alternatives: More flexible than fixed-greedy-decoding-only systems (like some edge-deployed models) because it supports beam search and temperature sampling; however, slower than specialized streaming decoders (like Kaldi or Vosk) that use HMM-based decoding optimized for low-latency online processing.
Generates precise word-level timestamps by aligning decoded tokens to audio segments using the model's internal attention weights and token probabilities, enabling subtitle generation and fine-grained audio-text synchronization. The system decodes text at the segment level (30 seconds), then uses token timing information to map each word back to its position in the original audio, producing timestamps accurate to ~100ms granularity.
Unique: Derives word-level timestamps from the model's token-to-audio alignment without a separate alignment model, using the decoder's implicit timing information from mel-spectrogram frame positions. The approach avoids the need for external forced-alignment tools (like Montreal Forced Aligner) by leveraging the model's learned audio-text correspondence.
vs alternatives: Simpler than forced-alignment pipelines (Montreal Forced Aligner + Whisper) because it uses a single model; however, less accurate than specialized alignment models trained specifically on timing prediction, and requires custom implementation to extract timing metadata from the model.
Provides six model sizes (tiny, base, small, medium, large, turbo) with parameter counts ranging from 39M to 1550M, enabling users to select optimal speed-accuracy tradeoffs based on hardware constraints and latency requirements. Each model has English-only variants (tiny.en, base.en, small.en) that sacrifice multilingual capability for 10-40% speed improvement, and the turbo model (809M) optimizes large-v3 for 8x faster inference with minimal accuracy degradation but no translation support.
Unique: Provides both multilingual and English-only variants for smaller models (tiny, base, small) to enable language-specific optimization, whereas most speech recognition systems offer only a single model per size. The turbo model represents a specialized optimization of large-v3 for inference speed using knowledge distillation or quantization techniques, not just parameter reduction.
vs alternatives: More granular model selection than Google Cloud Speech-to-Text (which offers only one model per language) and more transparent about speed-accuracy tradeoffs than commercial APIs that hide model details; however, requires manual model selection and management, whereas cloud services handle this automatically.
+3 more capabilities