AudioCraft vs Kyutai Moshi

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

	AudioCraft Audio	Kyutai Moshi Audio
Tagline	Meta's open-source research toolkit for generating music and sound effects from text via a single autoregressive language model.	Open-source, full-duplex speech-to-speech foundation model with sub-200ms latency
Category	Audio	Audio
Pricing	Free· Free and open source; self-hosted	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).
Model	MusicGen, AudioGen, EnCodec	Moshi (7B-class speech-text foundation model) + Mimi neural audio codec, in-house by Kyutai
Editorial score	8.2 / 10	—
Use cases	text-to-musicsound-effectsaudio-compressionresearchself-hosted-generation	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	Fully open source with code and weights published by Meta Single-LM architecture is simpler than diffusion pipelines Covers music, sound effects, and neural codec in one repo Strong baseline used widely in audio ML research No usage fees once self-hosted	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	No hosted product or managed API - you must run it yourself Model weights typically CC-BY-NC, limiting commercial use Requires GPU and ML tooling to operate Output quality trails newer commercial models like Suno v4	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
Website	audiocraft.metademolab.com	kyutai.org

Pick AudioCraft if

✅ Fully open source with code and weights published by Meta
✅ Single-LM architecture is simpler than diffusion pipelines
✅ Covers music, sound effects, and neural codec in one repo
✅ Strong baseline used widely in audio ML research

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

Compare a different pair →