Not sure why it did that. Its own rationale (which is highly suspect, but the only lead I have) is that it defaults to dense style if it has to write a file in a single go. May be a kernel of truth somewhere in there.

It looked gross and minimized, the result was awesome but the code looked pretty awful visually

There's too many confounding variables here, randomness just one of them. So I don't think of it as a definitive test (and reliable ordering), just another data point (along with actual benchmarks, pelicans, etc) to get a sense of the capabilities.

For example, I managed to get something out of DeepSeek 4 Flash quantized to 2-bit with Antirez' DwarfStar, used via Pi. Almost kinda worked! :) Which makes me optimistic for using local models for serious development soon - I'd say within a year.

After some interrogation, here's how it organized the work:

1. Design workflow (rts-game-design, 11 agents, ~13 min) ran first, produced SPEC.md + DESIGN.md:

1.1. Proposals (3 parallel agents): each designed a complete RTS from a different philosophy

1.2 Judge (1 agent): evaluated all three and synthesized one unified design, committing to specific numbers (costs, HP, map size, etc.).

1.3 Deep-dives (6 parallel agents): each wrote an implementation-ready spec for one subsystem, all consistent with the chosen design

1.4 Synthesis (1 agent): merged the design + all six subsystem specs into one conflict-free master spec

2. Code-review workflow (rts-code-review, 25 agents, ~5 min), ran after the main agent had written and tested the code:

2.1 Review (6 agents, read-only Explore type): each scrutinized one dimension and returned structured findings.

2.2. Verify (19 agents): every finding got its own skeptic agent told to try to refute it, Result: 19 flagged → 16 confirmed, 3 rejected as non-bugs.

What the main agent did in the main loop:

- Wrote all ~2,400 lines of index.html by hand from the spec.

- All browser testing/debugging via headless Chrome (I told it to use rodney by @simonw, love the tool :)

- Applied all 16 fixes from the review and re-verified them in the browser.

But I just vibe-coded a handy list of all the tests I did (unfortunately without the commentary I usually leave in social media posts -- I should add those at some point): https://senko.net/vibecode-bench/

Between the two, Opus 4.8 seems more capable. But, I suspect the harness plays a large role here. It's possible the result would be as good if Codex ran 10+ agents and spent an hour on it.

OpenAI and Anthropic usually fast-follow each other, so I wouldn't be surprised if Codex got the same capability in a couple of days (and even an update to the model), then it'll be a better test.

Sooo, let's say, winging it, vibes-based: 85% for Opus 4.8, 75% for GPT 5.5. Compare with GPT 5.3 (let's say 25%) here: https://senko.net/vibecode-bench/2026/rts-codex-5.3.html