We made Grok 4.5, GPT-5.5, and Claude build the same apps

Excellent idea, most terrible execution. Comparison are completely subjective, problem space is too simplistic for today's AI, the resultstable simply ignores that a face isn't a cube (therefore, gpt 5 shouldn't have 100% success) and the retry is uneven. Also, given the random nature of AI, sampling once each model isn't very scientific.

This feels like a kid trying to do science. The will is there, but lacks experience.

Half year ago I tried to use Codex, Claude and Gemini build the same scripts to automate various things on my machine. Claude was the clear winner back then, making the most reasonable assumptions, presenting results in the easiest-to-read format, writing runnable script with minimum dependency. Half year later I think Codex and Claude models have both advanced a lot, but Gemini is still lackluster. Gemini could catch problems when reviewing Claude/Codex's design plans and code, but it's hard to make Gemini make complex plans or implement complex code by itself.

I tried to one-shot the first test (the Rubik's Cube test) with LucidQuery's Swift model, to test it, as there are not much benchmarks about it and that they brag a lot about it, and I was pleasantly surprised to see it achieving a result similar to Grok 4.5 but in one shot (there is the same issue that if you scramble twice the solve button does not work anymore, but it got it in one shot).

Though it crunched most of the free quota, 47111 tokens, so I couldn't make multiple attempts.

So strange to write a whole post with Claude giving the best results and Grok consistently the worst, but awarding Grok the winner because at least it did the worst fastest?

I am 99% sure the post was written by AI

I have not used grok 4.5 yet, but the other pictures match my experience doing anything graphical with the other models that it cracks me up. gpt 5.5 has no design sense whatsoever. It cannot even make terminal output not look terrible. I've asked it to use colors and formatting in various ways and got goofy randomly colored output. opus 4.7 and later seemed to have an inuitive design sense by comparison - 2d or 3d. Fabel 5 is just rock solid.

Yes, subjective. But it matches my repeated experiences with these models for what it is worth.

I’m spending a significant portion of my day waiting for agents to execute.

What’s more interesting to me than time-to-first token or latency is the time it takes for the agent to execute, from starts to finish, excluding when it’s waiting on a human.

If you like this kind of comparison, we have an arena of 52 apps one-shotted across 21 models here: https://arena.logic.inc/

I keep it pretty up to date (tomorrow Grok 4.5 and Sonnet 5 should be pushed).

> The receipts: speed and cost

I don't get why cost per reply is at all relevant here?

Why do so few who attempt comparisons actually compare dollars per task.

Love the idea, I think more complex games would show the gap in ability better.

Do it again but this time get them to make a multiplayer online Jetmen REVIVAL game. Online play is key, because it's very complex. Jetmen is a good game for this since it has physics and customization that's complex enough but still simple.

Why not wait one more day for GPT-5.6?

I'd like to see the comparisons with DeepSeek, Qwen, Mimo, Kimi and GLM

Isn’t the number of turns most important? Some agents take repeated input, while others can mostly one-shot what I’m looking for.

I get the point of this demo but if instructions are clear, tech stack related resources are available, then the models do not differ as much.

I use different models all the time. And mostly lower cost ones. I do not know how people write software these days, but I have clean instructions, usually in Epics and they have Tasks.

I have been using DeepSeek V4 Flash for much of my coding in https://github.com/brainless/akar for example. Planning is mostly done by Qwen latest (in opencode) or Sonnet.

For my commercial, client work I use Claude but barely use Opus. Sonnet does most of the work. For a recent project, I went through a 35 page PRD in about 4 weeks, that includes client calls, changes, Ecpi/Task generation, a massive test suite, deployment.

Chat got ones were slow on Firefox mobile

Interesting that all four models converge on such similar designs, for such short prompts.

Could probably do this with a much older model given that it's something that probably has thousands of github repositories for source code to do so.

Barring the retry thing, n=1 on all models? Am I misreading, or is this a joke?

Variance in quality on these things is so, so high.

“The honest headline:”

Written by Claude. Ugh. If it’s worth publishing, it’s worth proofreading, folks.

This is disgustingly biased. The conclusion is that Grok holds its own?! There was zero evidence of that.

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Too nice to Grok, if there are really cost savings it should say how much each of the three demos cost so we can judge if it's worth the lower quality (probably not). The time to complete each would also be interesting.

Tried at work , this release def a moment I will remember. My work is not the same . The model is the first model that offer exactly as I want :

For hard tasks , that needs precision I will wait and pay expensive tokens

For everything else , query data , logs, rolling out releases , I’m using grok and it’s much better vs other tools and much cheaper too .