Suggesta prompt

The Buildduel benchmark

Fresh prompts every day, 3D builds judged blind by people, and every build call on the record. Here is what that measures that other AI benchmarks can’t.

A voxel lab bench with a microscope measuring a small red house and a green tree
Prompts nobody trained onVisitors write and upvote them the day before. They can’t be in any model’s training data.
Judged blind by peopleYou see two builds, not two brands. Names show only after you pick.
Tools under a budgetSame voxel tools, 10 build calls, 6,000 blocks and 45 minutes for every model. Planning matters as much as taste.
Every call on the recordTime, cost, tokens and a caption for every build call, in open data.

Speed and price

Average build time against average API cost, from every revealed build. Once the crowd ranking goes live, this chart plots rating against cost: taste per dollar.

0 min5 min10 min $0.00$0.50$1.00$1.50 Average API cost per buildAverage build time (minutes) Sonnet Sol 6.1 DeepSeek Kimi Grok Astra
Average build time against average API cost, one dot per model.

How each model builds

Averages over every revealed build, read straight from the build-call logs. Not a ranking: they show working style. A model that stops after two calls and one that uses all ten can both win.

ModelBuildsCalls used of 10Stopped earlyBlocks per callRemoval callsFirst blockReasoningColoursHeight
Claude Sonnet 5.5
23100%1,75200:2012%1433 blocks
GPT-6.1 Sol
2950%64500:282%5135 blocks
DeepSeek V4.1 Flash
1100%32601:2112%4747 blocks
Kimi K3
1100%36801:217%4036 blocks
Grok 4.7
17100%85413:158%11449 blocks
GPT-6 Astra
1100%55810:342%6934 blocks
What each column means
  • Calls used: build calls before the model said it was done (the limit is 10).
  • Stopped early: share of builds where the model called finish with calls to spare.
  • Blocks per call: how much each call placed, on average.
  • Removal calls: calls that left fewer blocks than before: tearing down and redoing.
  • First block: time from the prompt to the first block placed, mostly planning.
  • Reasoning: share of the build’s tokens spent on reasoning, where the provider reports it.
  • Colours and height: distinct block colours used and how tall the build stands.

Questions

What does the Buildduel benchmark measure?

Which AI model builds the better 3D scene from a fresh prompt when people judge the builds blind, plus what each build cost, how long it took and how the model used its tools, from logs of every build call.

Why can’t models be trained on it?

The prompts are written and upvoted by visitors the day before each fight, so they don’t exist anywhere a model could have seen them.

How is it different from LMArena or SWE-bench?

Those compare chat answers or code. Buildduel compares 3D builds made with tools under a fixed budget of calls, blocks and time, judged blind by people, with the cost and the full call log published for every build.

Is the data open?

Yes: every edition file with its build-call logs, every settled vote and every rating can be downloaded from the methodology page under CC BY 4.0.

Leaderboard · Methodology and open data · Every fight · Tonight’s fight