Top is Opus 5.5, followed by Fable.
- Corrupted/dropped Gemini thought_signature (reasoning_details) in tool loops
- Blinded model.api.id = "contestant" bypasses gemini.txt and serves Claude's prompt (default.txt)
- Anthropic-only prompt caching
The full report is here, if you're interested: https://docs.google.com/document/d/1-HqujXuDRJSWJIqEvmctcINr...Prompt was:
> I read on hacker news the following, which does not match my expectations. Check if something in the harness or bench is hurting gemini perf.
followed by your post.
Argon in low and medium mode had higher quality results than it did when running in high mode, almost every time. My tasks involved the agent finding some details in a raster image and then mark it up with predictions about what it found.
Argon on high would do the work, and then a whole bunch of other stuff i did not ask for. All that extra broke everything else downstream from the analysis.
However, doing the same work with Argon in low effort was on par with results from Haiku 5.5 (xhigh) and GPT-6.1-Terra (medium). So, Argon low works better than Argon high, but for my tasks -- a very weak model relative to how google positions it.
I'm ready for Geimini Unobtanium!
what does "local" coding agents mean? Surely you're not hosting it..