| 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | $5/mo | — |
| Capabilities | 16 decomposed | 11 decomposed |
| Times Matched | 0 | 0 |
Converts input text to natural-sounding speech audio using one of three specialized models (Eleven v3 for emotional expressiveness, Multilingual v2 for stability on long-form content, or Flash v2.5 for low-latency production). The system processes text character-by-character with per-character credit consumption (1 credit per character for standard models, 0.5-1 for Flash variants), respecting model-specific input limits (5k-40k characters) and language coverage (29-70+ languages). Output is streamed or returned as PCM audio at 44.1kHz with quality tiers from 128kbps (free/starter) to 192kbps (pro+).
Unique: Offers three distinct TTS models optimized for different use cases (emotional expressiveness vs. stability vs. latency) with character-level credit consumption and per-model input limits, enabling cost-conscious developers to choose the right model for their latency/quality tradeoff. Flash v2.5's 40k character limit and 0.5-1 credit per character pricing is significantly more efficient than competitors for long-form synthesis.
vs alternatives: Faster and cheaper than Google Cloud TTS or AWS Polly for long-form content (40k character limit vs. 5k-10k competitors) and more emotionally expressive than traditional TTS engines, though character-based pricing can exceed per-minute competitors at scale.
Enables users to clone a voice from audio samples (instant cloning) or create a professional voice clone with higher fidelity through a managed process. Instant Voice Cloning (Starter tier+) accepts short audio samples and generates a cloned voice usable immediately in TTS synthesis. Professional Voice Cloning (Creator tier+) involves a more rigorous process with quality assurance, producing voices suitable for commercial use. Both methods integrate with the standard TTS pipeline, allowing cloned voices to be used across all three TTS models with the same character-based credit consumption.
Unique: Provides two-tier voice cloning (instant for rapid prototyping, professional for commercial quality) integrated directly into the TTS pipeline, allowing cloned voices to be used across all three TTS models without separate configuration. The instant cloning path enables same-day voice creation without manual review, differentiating from competitors requiring longer approval cycles.
vs alternatives: Faster instant voice cloning than Google Cloud or AWS alternatives (no manual review required) and more integrated with TTS synthesis pipeline, though professional cloning timeline and quality standards are not publicly documented.
Provides qualifying startups with 12 months of free access plus 33 million characters of free TTS credits (equivalent to ~33,000 minutes of audio). The program is designed to enable early-stage companies to build voice features without upfront costs. Eligibility criteria and application process are not fully documented. Grants are distributed through the ElevenLabs website or partner programs (Y Combinator, Techstars, etc.).
Unique: Offers substantial free credits (33M characters) plus 12 months of free access to qualifying startups, enabling early-stage companies to build voice features without upfront costs. The program is designed to build long-term customer relationships and reduce barriers to voice feature adoption.
vs alternatives: More generous than Google Cloud or AWS startup programs in terms of voice synthesis credits, though eligibility criteria and application process are less transparent than competitors.
Enables team collaboration through workspace management with role-based access control and seat allocation. Different pricing tiers provide different numbers of workspace seats: Scale tier includes 3 seats, Business tier includes 10 seats, and Enterprise tier includes custom seat allocation. Seats enable multiple team members to access the same workspace, projects, and voice library. The system supports consolidated billing and team-level usage tracking. Workspace features include project organization, shared voice library access, and collaborative content creation.
Unique: Provides workspace-level collaboration with tiered seat allocation (3 seats at Scale, 10 at Business, custom at Enterprise) and consolidated billing, enabling team-based voice synthesis workflows. The feature is designed for teams and agencies rather than individual creators.
vs alternatives: More integrated team management than basic multi-user support, though workspace collaboration features are not fully documented compared to competitors like Google Cloud or AWS.
Modifies voice characteristics (pitch, speed, tone, accent) of existing audio recordings through neural voice transformation, enabling voice customization without re-recording or voice cloning. The voice changer applies learned transformations to match target voice characteristics while preserving original speech content and intelligibility, suitable for accessibility adjustments, creative effects, and voice personalization.
Unique: Voice modification enables characteristic adjustment without re-synthesis or cloning, using neural transformation to preserve original speech content while changing voice properties. Competitors lack equivalent integrated voice modification.
vs alternatives: More flexible than voice cloning for minor adjustments, and faster than re-synthesis for voice characteristic changes.
Implements a credit-based pricing model where each API operation consumes credits based on input size and operation type (1 character = 1 credit for standard TTS, 0.5-1 credit per character for Flash models depending on tier). Credits are allocated monthly per subscription tier (10k-6M credits/month), with unused credits rolling over for up to 2 months, enabling cost predictability and budget management. Developers can monitor credit consumption per request and optimize usage patterns to reduce costs.
Unique: Credit-based pricing with 2-month rollover enables cost predictability and budget smoothing, while per-character pricing (1 character = 1 credit) provides transparent, granular cost tracking. Competitors (Google Cloud, AWS) use per-request or per-minute pricing with less granular cost visibility.
vs alternatives: More transparent and predictable than per-request pricing, with credit rollover enabling budget flexibility for variable usage patterns.
Maintains a persistent voice library where cloned voices, designed voices, and pre-built voices are stored as reusable profiles with unique identifiers. Developers can create, organize, and manage voice profiles across projects, enabling consistent voice usage across multiple synthesis requests without re-cloning or re-designing. Voice profiles support metadata tagging and organization, facilitating voice discovery and reuse at scale.
Unique: Voice library enables persistent voice profile storage and reuse across projects, with metadata organization and discovery. Competitors lack equivalent voice profile management, requiring voice cloning or design per-request.
vs alternatives: More efficient than per-request voice cloning or design, enabling consistent voice usage and team collaboration at scale.
Generates speech and text content across 29-90+ languages depending on operation (TTS supports 29-70+ languages, STT supports 90+ languages), with automatic language detection for input content. The system automatically selects appropriate language-specific models and processing pipelines based on detected language, enabling seamless multilingual workflows without explicit language specification. Supports language mixing in some contexts (e.g., code-switching in dialogue).
Unique: Automatic language detection across 90+ languages (STT) eliminates explicit language specification, enabling seamless multilingual workflows. Competitors require explicit language selection per request.
vs alternatives: More user-friendly than language-specific APIs, with automatic detection reducing developer burden for multilingual applications.
+8 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 ElevenLabs API 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