r/ClaudeAI benchmarked Claude’s 3D skills on a galloping horse — Max effort took an hour, Haiku took 61 seconds

Some benchmarks need a lab. This one needed a horse. In the early hours of Friday morning, a r/ClaudeAI user named u/bursinru posted a seven-second video: the same prompt — a low-poly 3D horse running in a seamless loop, built procedurally in Three.js — run through five of Claude’s effort tiers, from Low to Max. The Max tier took a full hour. The video asks a single question: which version do you think is the best?

By mid-morning the thread sat at roughly 1,300 upvotes and 230 comments, and it had become something more interesting than one person’s test: an open, cross-model horse race. Commenters started entering their own contenders — a 61-second Haiku attempt, a locally-run Qwen 3.8, a 14-minute GPT-5.6 Sol High run — and the top of the thread reads less like a benchmark than a trackside crowd arguing over form.

The test: one prompt, five effort tiers#

Effort tiers are the knob that trades compute and time for quality: run the same request longer and harder, get a better result. The poster ran the horse prompt at Low, Medium, High, Extra, and Max, then stitched the outputs into a single montage. The headline number is the Max tier’s: a full hour of effort for a seven-second loop of a polygon horse.

And a clarification the thread itself keeps making: this is Claude writing JavaScript, not a diffusion model painting pixels. Every horse is procedurally generated Three.js code — geometry, animation loop, the works. When the poster dropped a “Bonus video” in the comments, the verdict was affectionate but blunt: “Hahaha best one. And I almost mean it, they all are bad but this one is funny.”

Then the comments turned it into a horse race#

The best benchmark of the thread may not be the original post at all — it’s the replies, where commenters ran the same horse prompt on other models and posted their attempts:

The Haiku entry is the thread’s emotional winner: a minute of effort producing a horse the crowd genuinely loved, against an hour of Max-tier compute. Whether that says more about the tiers or about the prompt is exactly the argument happening in the replies — which is what makes it a good benchmark.

What the skeptics got right#

The sharpest critique came from u/EnumeratedArray: “Why does everything low poly in threejs that Claude makes look exactly the same?” — with the reply, “It’s becoming the new Generic.” It’s a real signal, not just snark: when every output shares a house style, that tells you something about the model’s priors, and it’s the kind of thing a formal benchmark with a single pass/fail would never surface.

The thread also did its own fact-checking. One commenter asserted a large environmental cost for a task using 28 million tokens; others ran the numbers and found the claim badly off — roughly 2 kWh, “the cost of running maybe 4 GPUs,” with the original figures apparently coming from treating every token as fresh output. The correction happened in public, in the open, within the hour.

The honest caveats still apply: one prompt, one judge, no controls, and the judging criterion is vibes. But that is also the point. This is how real users actually compare models — fast, public, and ruthless about diminishing returns.

What to watch#

Community benchmarks like this are becoming the informal scoreboard for code-generating models — faster than lab evals, honest about failure, and allergic to marketing. An hour of Max effort versus 61 seconds of Haiku is the kind of comparison no product page would ever publish, and it landed harder than most leaderboard updates.

It’s the same wave we’ve been tracking all week: Claude’s code generation keeps producing these public experiments, from the playable fishing-island game built in eight hours to the SNES boss battle coded from scratch. The horse race is the smallest of the three — and maybe the most revealing, because the only thing being measured is taste.