Fireflies.ai vs Kyutai Moshi

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

	Fireflies.ai Audio	Kyutai Moshi Audio
Tagline	AI meeting assistant that joins calls, transcribes them, and turns the talk into searchable notes and action items.	Open-source, full-duplex speech-to-speech foundation model with sub-200ms latency
Category	Audio	Audio
Pricing	Freemium· Basic: $10 · Pro: $20 · Enterprise: Contact sales	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	Multi-model (proprietary ASR + LLM layer)	Moshi (7B-class speech-text foundation model) + Mimi neural audio codec, in-house by Kyutai
Editorial score	8.2 / 10	—
Use cases	meeting transcriptioncall summariesaction item extractionsales call analysisrecruiting notesvoice search	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	Joins all major meeting platforms plus dialers and uploaded files with one workflow Strong CRM/ATS sync (Salesforce, HubSpot, Greenhouse) keeps notes out of inboxes AskFred chat and AI Apps make weeks of past calls actually searchable Free tier is genuinely usable for solo evaluators Public API for programmatic transcription and summary retrieval	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	Visible bot attendee can feel intrusive in client or candidate calls Conversation intelligence and unlimited storage are gated to Business+ Summary quality drops on noisy multi-speaker calls in less-supported languages Closed-source, fully cloud-hosted - no self-hosted option	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	fireflies.ai	kyutai.org

Pick Fireflies.ai if

✅ Joins all major meeting platforms plus dialers and uploaded files with one workflow
✅ Strong CRM/ATS sync (Salesforce, HubSpot, Greenhouse) keeps notes out of inboxes
✅ AskFred chat and AI Apps make weeks of past calls actually searchable
✅ Free tier is genuinely usable for solo evaluators

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

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