Kyutai Moshi vs Otter.ai

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

	Kyutai Moshi Audio	Otter.ai Audio
Tagline	Open-source, full-duplex speech-to-speech foundation model with sub-200ms latency	AI meeting notetaker that transcribes calls, summarizes them, and pulls out action items in real time.
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· Basic: $20 · Pro: $50 · Enterprise: Contact sales
Model	Moshi (7B-class speech-text foundation model) + Mimi neural audio codec, in-house by Kyutai	Proprietary speech + LLM stack
Editorial score	—	6.8 / 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	meeting-transcriptionmeeting-summariesaction-itemssales-call-noteslive-captions
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	Genuinely free tier covers casual meeting notes without a trial clock Joins Zoom/Meet/Teams as a bot and ships desktop bot-free recording Strong CRM and workspace integrations (Salesforce, HubSpot, Slack, Notion) MCP server lets Claude/ChatGPT query your meeting history directly
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 general developer API for programmatic transcription workflows Bot-in-meeting model can feel intrusive on sensitive calls Accuracy on heavy accents and crosstalk still lags dedicated ASR vendors
Website	kyutai.org	otter.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 Otter.ai if

✅ Genuinely free tier covers casual meeting notes without a trial clock
✅ Joins Zoom/Meet/Teams as a bot and ships desktop bot-free recording
✅ Strong CRM and workspace integrations (Salesforce, HubSpot, Slack, Notion)
✅ MCP server lets Claude/ChatGPT query your meeting history directly

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