| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | $0.65/hr | — |
| Capabilities | 13 decomposed | 11 decomposed |
| Times Matched | 0 | 0 |
Generates speech from text input using state-space model (SSM) architecture optimized for real-time streaming, delivering time-to-first-audio in 40-90ms depending on model variant (Sonic-Turbo: 40ms, Sonic-3: 90ms). Streams audio chunks progressively to client as text is processed, enabling interactive voice agent applications with near-instantaneous speech output. Uses character-level pricing (1 credit per character) with support for 42 languages and dynamic voice control parameters.
Unique: Uses state-space model (SSM) architecture instead of traditional transformer-based TTS, enabling 40-90ms time-to-first-audio with streaming output. This architectural choice allows progressive audio generation without waiting for full sequence completion, critical for interactive applications. Sonic-Turbo variant achieves 40ms latency (claimed as 'twice as fast as the blink of an eye'), positioning it as fastest in category.
vs alternatives: Achieves 2-4x lower latency than transformer-based TTS systems (e.g., Google Cloud TTS, Azure Speech Services) by using SSM architecture with streaming-first design, making it the only viable option for sub-100ms voice agent interactions.
Enables fine-grained control over emotional tone and prosodic characteristics of generated speech through inline text tokens and voice parameters. Supports explicit emotion markers like '[excited]' and '[sad]' embedded in input text, allowing dynamic emotional expression within a single speech generation request. Works in conjunction with voice selection and voice localization to modulate pitch, pace, and emotional coloring of output audio.
Unique: Implements emotion control through inline text tokens ('[excited]', '[sad]') rather than separate API parameters, allowing emotion changes mid-utterance without multiple API calls. This token-based approach integrates emotion control directly into the text input stream, enabling natural emotional transitions within continuous speech generation.
vs alternatives: Provides more granular, mid-utterance emotion control than cloud TTS systems (Google Cloud, Azure) which typically apply emotion at the request level; token-based approach allows emotional expression to follow narrative flow without API call overhead.
Implements credit-based pricing model where TTS generation costs 1 credit per character of input text, with additional credits for advanced features (voice cloning, localization, infilling). Credits are allocated monthly based on subscription tier (Free: 20K, Pro: 100K, Startup: 1.25M, Scale: 8M, Enterprise: custom) and do not roll over between months. This granular pricing model enables transparent cost prediction and prevents surprise bills.
Unique: Uses character-level credit granularity (1 credit per character) rather than per-request or per-minute pricing, enabling precise cost prediction based on input volume. Advanced features have separate credit costs (voice cloning: 1M credits training + 1.5 credits/character; localization: 225 credits; infilling: 300 credits + 1 credit/character).
vs alternatives: Provides more transparent, granular pricing than per-request models; character-level pricing aligns cost with actual usage, unlike per-minute pricing which penalizes longer utterances.
Provides native integrations with popular voice agent frameworks (Pipecat, Rasa), real-time communication platforms (LiveKit, Tencent RTC, Twilio), and specialized voice agent services (Thoughtly, Vision Agents by Stream). Integrations handle authentication, streaming audio transport, and request/response marshaling, enabling developers to use Cartesia TTS/STT without building custom API clients.
Unique: Provides native integrations with multiple voice agent frameworks (Pipecat, Rasa) and RTC platforms (LiveKit, Twilio, Tencent RTC), reducing integration effort compared to building custom API clients. Integrations handle streaming audio transport and request marshaling transparently.
vs alternatives: Reduces integration effort compared to competitors requiring custom API client development; pre-built integrations with popular frameworks enable faster time-to-market for voice agent projects.
Provides separate credit allocation for voice agent deployments through 'agent credits' distinct from model credits. Agent credits are prepaid amounts (Free: $1, Pro: $5, Startup: $49, Scale: $299, Enterprise: custom) that fund voice agent operations, enabling separate cost tracking and budget management for agent-based systems vs direct API usage. Mechanism for converting agent credits to API calls is not documented.
Unique: Implements separate agent credit system for voice agent deployments, enabling cost tracking and budget management independent from direct API usage. This architectural choice allows organizations to manage voice agent costs separately from other API usage.
vs alternatives: Provides separate cost tracking for voice agents vs direct API usage, enabling better budget allocation and cost visibility than unified credit systems; prepaid agent credits enable predictable monthly costs.
Supports two voice cloning modes: Instant Voice Cloning (IVC) requiring zero training credits, and Professional Voice Cloning (PVC) requiring 1M credits for one-time training plus 1.5 credits per character of generated speech. IVC uses speaker embedding extraction from reference audio to immediately synthesize speech in that voice without training. PVC trains a custom voice model on reference samples for higher quality and consistency, suitable for production voice agent deployments.
Unique: Offers dual voice cloning modes: IVC (zero training cost, immediate) and PVC (1M credit training, higher quality). This two-tier approach allows rapid prototyping with IVC while enabling production-grade voice consistency with PVC. The credit-based pricing for training (1M credits) is transparent and predictable, unlike some competitors offering opaque training processes.
vs alternatives: Provides faster voice cloning than Google Cloud Speech-to-Text voice cloning (which requires manual training and approval) and more transparent pricing than ElevenLabs (which uses opaque 'voice cloning credits'); IVC mode enables immediate voice cloning for prototyping without training overhead.
Generates laughter and other non-speech vocalizations (e.g., sighs, gasps) by embedding special tokens like '[laughter]' directly in input text. The synthesis engine recognizes these tokens and generates appropriate audio vocalizations that integrate seamlessly with surrounding speech, enabling natural conversational dynamics in voice agents and interactive media.
Unique: Implements laughter and vocalizations as inline text tokens ('[laughter]') rather than separate API calls or post-processing, allowing vocalizations to be generated as part of continuous streaming speech without latency overhead. This token-based approach treats vocalizations as first-class elements of the speech synthesis pipeline.
vs alternatives: Provides more natural vocalization integration than systems requiring separate API calls for laughter generation; token-based approach ensures vocalizations flow naturally with surrounding speech without timing gaps or synchronization issues.
Enables regional accent and localization control for synthesized speech through voice localization parameters, allowing the same voice to be rendered with different regional accents or pronunciation patterns. Implemented as a one-time 225-credit cost per localization variant, suggesting a voice model fine-tuning or adaptation approach. Supports 42 languages with localization variants available for each.
Unique: Implements voice localization as a one-time 225-credit training/adaptation cost per variant, suggesting voice model fine-tuning on regional speech data. This approach trades upfront cost for consistent, high-quality accent rendering, rather than real-time accent morphing which would be lower quality.
vs alternatives: Provides more authentic regional accents than real-time accent morphing approaches (which often sound artificial); one-time training cost ensures consistent accent quality across all generations, unlike parameter-based accent control which may degrade voice naturalness.
+5 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 Cartesia 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