Moshi AI vs CassetteAi

Moshi AI is a speech-native conversational model from Kyutai, a Paris-based open-science research lab. Instead of chaining speech recognition, text generation, and text-to-speech, Moshi processes audio directly and holds full-duplex voice conversations with minimal latency.

Its multi-stream design runs separate channels for the user, Moshi's spoken output, and an Inner Monologue text stream that improves coherence. That setup lets Moshi listen and talk at the same time, handle overlaps, interruptions, and backchanneling like a real conversation rather than rigid speaker turns.

Moshi is built on Helium, a 7B language model, and Mimi, Kyutai's neural audio codec. Weights and inference code ship for PyTorch, Rust, and MLX, and you can try it in the browser at moshi-chat.kyutai.org. Researchers, voice AI developers, and anyone building real-time spoken interfaces will find the most value here.

CassetteAi is a real-time audio generation API for developers who need music, sound effects, or speech inside apps and games. Its models render a 30-second music sample in under 2 seconds and a full 3-minute track in under 10, at 44.1 kHz stereo. One SDK covers all three modalities through the same call shape.

Cloud audio APIs usually add round-trip latency that breaks interactive experiences. CassetteAi targets on-device inference with sub-50 millisecond time-to-first-audio and pay-per-second billing instead of monthly seats. The homepage cites 23 ms first-sample latency and deterministic seeds so game loops can re-roll SFX per frame without drift.

Music costs $0.02 per output minute and SFX costs $0.01 per generation, with no tier commitments. You call the models through fal.ai using JavaScript, Python, or cURL. Text-to-speech with zero-shot voice cloning is listed as launching soon.

Game studios, creator tools, and real-time media pipelines are the stated audience. Pixl Technologies runs the company from Salt Lake City, with engineering spread across North America and Europe.