Open this lesson in your favourite AI. It'll walk you through the why, explain the demo, and quiz you on the try-it list.
The AI music tooling landscape in 2026 has consolidated around a handful of workhorses. Suno and Udio dominate full-song generation (vocals + instruments in a single prompt); MusicGen and Stable Audio are the go-to open-weights options for instrumentals; ElevenLabs owns AI voice/vocals (spoken + increasingly sung); Riffusion and YuE handle style transfer + remixes; MusicLM by Google exists but rarely wins on quality. Understanding what each is good at (and what it fails at) is the first hour of this course — everything downstream depends on picking the right tool for the right job. The 'AI music sounds bad' complaint almost always comes from someone using Udio for instrumentals or Suno for solo vocals, when a different tool would nail it in three prompts.
A capability matrix — six tools, six axes (vocal quality, instrumental depth, prompt control, style range, output length, cost). Print it, tape it to your wall, and consult before every session.
Use these three in order. Each builds on the one before.
In one paragraph, explain the difference between full-song generators (Suno/Udio) and instrumental generators (MusicGen/Stable Audio).
Walk me through why prompt control is stronger in Stable Audio than in Suno — the model architecture difference and what it buys.
I want to make a 3-minute atmospheric electronic track with clean female vocals and a specific 90 BPM. Which tool(s) would I use and in what order — full-song, or stem-by-stem?
# Capability matrix — subjective scores, 1-5, as of Jul 2026.
# Update as models drift.
tools = [
# tool, vocal, instrumental, prompt_control, style_range, output_len, cost
("Suno v4", 5, 4, 3, 4, 3, "$8/mo unlim"),
("Udio", 5, 4, 3, 4, 3, "$10/mo"),
("MusicGen (Meta)", 1, 4, 2, 3, 2, "free / self-host"),
("Stable Audio", 2, 4, 4, 4, 4, "$12/mo"),
("ElevenLabs", 5, 1, 4, 3, 5, "$5-99/mo"),
("Riffusion", 3, 4, 3, 5, 3, "$8/mo"),
]
# What each is FOR (and where they lose)
verdicts = {
"Suno v4": "Full song, radio-ready sound. Fails on genre precision (asked for shoegaze, got pop).",
"Udio": "Full song, closer to indie/electronic sound. Fails on strict tempo/key adherence.",
"MusicGen": "Instrumental loops. Fails on anything > 30s or with vocals.",
"Stable Audio": "Cinematic textures + long-form instrumentals. Fails on catchy melodies.",
"ElevenLabs": "Vocal cloning + expressive TTS-to-song. Fails on musical instruments.",
"Riffusion": "Style transfer + remix. Fails as a first-choice for original composition.",
}
print(f"{'Tool':17s} {'Voc':>3} {'Inst':>4} {'Prpt':>4} {'Sty':>3} {'Len':>3} Cost")
for t, v, i, p, s, l, c in tools:
print(f"{t:17s} {v:>3} {i:>4} {p:>4} {s:>3} {l:>3} {c}")
print()
for t, verdict in verdicts.items():
print(f"{t}: {verdict}")
python3 main.py