Moshi AI vs Loudly

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.

Loudly turns text prompts into royalty-free tracks you can remix, master, and release to Spotify without leaving one studio. Pick proprietary models like VEGA for instrumentals (three quality tiers) or MANTA 1 for full songs with lyrics and vocals, then export MP3 or WAV files with a commercial license attached.

Where generic AI music tools stop at a single generator, Loudly chains generation, AI Remixer, stem splitting, FX, mastering, and Loudly Distro in one workflow. Competitors like Soundraw or Mubert focus on background tracks; Loudly adds vocal song generation via MANTA 1, a distribution service that pays 100% streaming royalties, and a Music API for embedding generation in third-party apps.

YouTubers, podcasters, indie game developers, and ad agencies use Loudly for social-safe background music, client campaign audio, and catalog releases on Spotify and Apple Music. Personal and Pro plans include Distro Plus on annual billing, releasing up to 200 tracks per month across 50+ platforms.