Kyutai Moshi vs Vapi

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

	Kyutai Moshi Audio	Vapi Audio
Tagline	Open-source, full-duplex speech-to-speech foundation model with sub-200ms latency	Developer platform for building, deploying, and scaling production voice AI agents
Category	Audio	Audio
Pricing	Free· 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).	Freemium· Build: Usage based · Scale: Contact Us
Model	Moshi (7B-class speech-text foundation model) + Mimi neural audio codec, in-house by Kyutai	Model-agnostic: OpenAI GPT-4o, Anthropic Claude, Google Gemini, Groq, DeepSeek, Llama; STT/TTS via Deepgram, ElevenLabs, PlayHT, Cartesia, Azure
Editorial score	—	—
Use cases	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	AI phone receptionistOutbound lead qualification callsAppointment scheduling and remindersVoice-based customer supportOrder taking and reservationsIVR replacementVoice-driven surveys and market researchDebt collection and payment remindersInsurance intake callsVoice interface for internal tools
Pros	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	Model-agnostic stack lets you swap LLM, STT, and TTS providers per assistant and bring your own API keys Handles the real-time voice plumbing (interruption detection, endpointing, barge-in, backchanneling) that is painful to build from scratch First-class REST API and SDKs across Node, Python, Web, iOS, Android, Flutter, and React Native Built-in telephony: provisions phone numbers, supports SIP trunks, and covers inbound and outbound calls Tool/function calling and 25+ prebuilt integrations (Salesforce, HubSpot, Zapier, Make, Cal.com, etc.) for real workflows Enterprise compliance path: SOC 2, HIPAA (add-on), PCI, SSO, RBAC, Zero Data Retention available Transparent per-minute platform fee with model costs passed through at cost rather than marked up opaquely
Cons	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	Total cost is hard to predict because platform fee, LLM, STT, TTS, and telephony are billed separately and stack up HIPAA compliance and Zero Data Retention are paid add-ons ($2,000/mo and $1,000/mo) rather than included Latency and voice naturalness ultimately depend on the third-party providers you choose, not Vapi itself Build plan retains call history only 14 days and chat history 30 days, so long-term analytics require your own pipeline Not a no-code tool — meaningful agents still require prompt engineering, tool wiring, and webhook code Concurrency beyond 10 lines costs $10/line/month, which adds up for high-volume outbound campaigns
Website	kyutai.org	vapi.ai

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

Pick Vapi if

✅ Model-agnostic stack lets you swap LLM, STT, and TTS providers per assistant and bring your own API keys
✅ Handles the real-time voice plumbing (interruption detection, endpointing, barge-in, backchanneling) that is painful to build from scratch
✅ First-class REST API and SDKs across Node, Python, Web, iOS, Android, Flutter, and React Native
✅ Built-in telephony: provisions phone numbers, supports SIP trunks, and covers inbound and outbound calls

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