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four models, three canvas tasks: sonnet 5 does more with less

Comic-style men in suits studying blueprints at a table while a figure welds on scaffolding in a neon-lit construction site models

sonnet 5 vs sonnet 4.6 vs opus 4.8 vs glm 5.2 – frontend tasks

dropped sonnet 5 into a quick test today. same three prompts to all four models, single-shot html/canvas, no edits:

• objects falling on a trampoline
• rockets playing tennis
• a slingshot breaking bottles

ranked by speed (total across the 3 tasks):

1. opus 4.8 – 15m 09s
2. sonnet 5 – 16m 05s
3. glm 5.2 – 27m 18s
4. sonnet 4.6 – 35m 06s

ranked by code shortness (total loc):

1. sonnet 5 – 1794
2. opus 4.8 – 2063
3. sonnet 4.6 – 2182
4. glm 5.2 – 3285

sonnet 5 came out on top here – leanest code overall and a near-tie for fastest

it was also the most creative. in every task it added something none of the others did:

– kept the trampoline vibrating after the objects landed
– drew a +1 next to the rocket that scored the point
– turned the slingshot to face the next bottle before each shot

opus 4.8 evaluated the code sonnet 5 produced. four things stood out:

• the sphere is a fake, and that's the smart move. the cube and star are real 3d meshes with proper culling and shading, but the ball is just a flat shaded circle. a lit sphere looks identical from every angle, so building it in 3d would burn compute for zero visible payoff. knowing where not to bother is its own kind of skill

• weight actually means something on the trampoline. the star is heavy, so it barely bounces and dents the mat hard. the ball is light, so it's lively and leaves a shallow dip. the three objects aren't just different shapes – they have different temperaments, and the physics is what gives them that

• the slingshot is framed like a shot, not just drawn. the handle is anchored below the bottom of the screen and runs off-frame, so it reads as something you're holding rather than a sprite parked in the scene. that's a staging instinct, not a rendering one

• the paddle ai forward-simulates the ball to predict where it'll land, then adds a deliberate error bias (roughly 1 in 5 shots is a real miss). that's why scoring looks natural instead of robotic – plus four distinct fault types with a catch-all so a rally never hangs without a result

bottom line: sonnet 5 does more with less. fastest tier, leanest code, and the only one that added small touches nobody asked for

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Portrait of Nick Trenkler, author at thehype.news

Nick Trenkler

Contributing Journalist & Content Creator at thehype. Nick holds a Bachelor’s degree in Journalism, Master’s in Marketing. He has been leading marketing for AI startups since 2024, with experience in media, digital strategy, and tech-focused content.

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