AI music generators now span foundation models that create full songs from text or voice prompts, services that produce adaptive background music, and toolkits for producers to generate toplines, MIDI and SFX. Google’s Lyria 3 (Gemini) is a prominent example in 2026 of major-model entrants aimed at higher-fidelity, multi-instrument generation; competing approaches include open-source foundation models and product-first platforms that target specific use cases. Representative tools highlight the landscape: ACE–Step combines an open-source foundation model with a browser-first consumer product to turn prompts into complete, royalty-free songs quickly; Flowfi focuses on adaptive lo-fi soundscapes that monitor and sustain user focus in real time; and Evoke Music’s relaunch as Amadeus Code packages FUJIYAMA AI SOUND generation with curated libraries, topline MIDI and SFX for creators. Together these illustrate common categories—text-to-music full-song generators, adaptive ambient/productivity music, and creative-assist libraries/DAW integrations. This topic is timely because model capacity, on-device inference, and product integration have matured enough to move AI music from experimental demos to practical workflows, raising practical questions about audio quality, controllability, licensing/royalty status, and DAW interoperability. When evaluating options, consider intended use (commercial release vs. background ambience), export formats (stems, MIDI), customization controls, and licensing terms. Ethical and copyright considerations remain active areas of concern, as do compute costs and latency for real-time applications. This overview helps compare the technical approaches and product tradeoffs among Lyria 3 (Gemini) and competing music models as of 2026.