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📖 The AI Tool Bible

Deepgram vs Kyutai Moshi

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

 Deepgram logo
Deepgram
Audio
Kyutai Moshi logo
Kyutai Moshi
Audio
TaglineProduction-grade speech-to-text, text-to-speech, and voice-agent APIs for real-time and batch audio.Open-source, full-duplex speech-to-speech foundation model with sub-200ms latency
CategoryAudioAudio
PricingFreemium· Free credits on signup; usage-based pricing; enterprise contracts availableFree· Free and open source. Models under CC-BY 4.0, code under MIT (Python) / Apache 2.0 (Rust). Self-hosted only — you pay your own compute (24GB+ GPU for PyTorch, or Apple Silicon via MLX).
ModelNova, Flux, Speak (proprietary)Moshi (7B-class speech-text foundation model) + Mimi neural audio codec, in-house by Kyutai
Editorial score7.9 / 10—
Use cases
speech-to-texttext-to-speechvoice-agentscall-center-analyticsreal-time-transcription
Real-time voice assistant prototypesResearch on full-duplex spoken dialogueOn-device voice interaction on Apple Silicon via MLXLow-latency conversational agents behind WebSocketNeural audio codec experimentation with MimiSelf-hosted voice interface for privacy-sensitive appsSpeech tokenizer for downstream audio LLM trainingInterruptible in-car or wearable voice UX
Pros
  • Very low latency streaming STT suitable for real-time voice agents
  • Self-hosted deployment option for regulated industries
  • Unified Voice Agent API bundles STT + TTS + LLM orchestration
  • Multilingual conversational STT via Flux across 10 languages
  • Truly full-duplex — handles interruptions, overlap and back-channels rather than rigid turn-taking
  • Sub-200ms practical latency on a single L4 GPU, well below third-party voice APIs
  • Fully open weights (CC-BY 4.0) plus MIT/Apache code — self-host with no per-minute billing
  • Ships with Mimi, a streaming neural audio codec that beats SpeechTokenizer and SemantiCodec
  • Multiple inference backends: PyTorch for research, Rust/Candle for production, MLX for on-device Mac/iPhone
  • Inner-monologue text prediction gives you a transcript alongside the audio stream for free
Cons
  • Pricing not transparent on the marketing site
  • Not open source; vendor lock-in on proprietary models
  • Product lineup (Nova vs Flux vs Agent) can confuse first-time evaluators
  • English-only voices at launch — no multilingual support out of the box
  • Knowledge and reasoning quality trail top text LLMs; it's a 7B-class model, not GPT-4o Voice
  • Requires a 24GB+ GPU for the reference PyTorch build; on-device is only viable via MLX on Apple Silicon
  • No hosted API or SaaS tier — you own the ops, scaling and safety filtering
  • Only two fixed synthetic voices (Moshiko/Moshika); no voice cloning or speaker conditioning in the release
Websitedeepgram.comkyutai.org
Pick Deepgram if
  • ✅ Very low latency streaming STT suitable for real-time voice agents
  • ✅ Self-hosted deployment option for regulated industries
  • ✅ Unified Voice Agent API bundles STT + TTS + LLM orchestration
  • ✅ Multilingual conversational STT via Flux across 10 languages
Pick Kyutai Moshi if
  • ✅ Truly full-duplex — handles interruptions, overlap and back-channels rather than rigid turn-taking
  • ✅ Sub-200ms practical latency on a single L4 GPU, well below third-party voice APIs
  • ✅ Fully open weights (CC-BY 4.0) plus MIT/Apache code — self-host with no per-minute billing
  • ✅ Ships with Mimi, a streaming neural audio codec that beats SpeechTokenizer and SemantiCodec