Kyutai Moshi vs Sesame

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

	Kyutai Moshi Audio	Sesame Audio
Tagline	Open-source, full-duplex speech-to-speech foundation model with sub-200ms latency	Conversational voice AI aiming to cross the uncanny valley with context-aware, emotionally aware speech.
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).	Free· Free research preview; consumer product pricing not announced
Model	Moshi (7B-class speech-text foundation model) + Mimi neural audio codec, in-house by Kyutai	Sesame CSM (1B / 3B / 8B)
Editorial score	—	8.0 / 10
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	conversational-voicetext-to-speechvoice-agentsambient-ai
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	Open-source weights under Apache 2.0 for the CSM speech model Distinctly natural, context-aware prosody compared to typical TTS Backed by serious original research with published benchmarks Free research preview available at app.sesame.com
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	No public commercial API - you self-host the open weights Pricing and productisation still vague; consumer app is invite-only Hardware (AI glasses) not shipping until 2027 Small model catalogue focused on English voice quality
Website	kyutai.org	www.sesame.com

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 Sesame if

✅ Open-source weights under Apache 2.0 for the CSM speech model
✅ Distinctly natural, context-aware prosody compared to typical TTS
✅ Backed by serious original research with published benchmarks
✅ Free research preview available at app.sesame.com

Compare a different pair →