Hands-On With Gemini 4: Top-Tier Model, Weak Multi-Agent Harness
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I spent a decent amount of time with Gemini 4 before launch. Overall, Gemini 4 is a big jump over previous Gemini models and it is the first non-OpenAI/Anthropic model to pass the Fable/Mythos/Astra bar. It is also much more pleasant and more agentic then previous Gemini models, without the weird personality issues that you often found in Gemini 3 series models. In fact, a big advantage is that has a very different personality and sense of taste than Claude or Astra, making it a good addition to the AI world. On the usefulness side, in some areas it is at least at par with OpenAI and Anthropic (it is quite creative and a good writer), but in others it lags, including at complex multi-agent work. It has a tendency to be too certain about its conclusions and a little lazy, issues not helped by the Antigravity harness not being quite as good as the competition. Still, I expect to see rapid improvements in model abilities using Gemini 4 as a base. Here is a fun example: "create the ultimate brutalist city builder. you should have lots of controls over buildings in ways that are delightful and inuitive to people who just want to play, but are sophisticated and deep for people who…

A practitioner-tested read on where Gemini 4 matches frontier peers and where it lags ( work, quality). Helps decide where it fits in a multi-model stack.
- eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.
- multi-agent — Using several AI agents on one problem — splitting work in parallel, checking each other, or filling different roles like planner and reviewer.
- agent harness — The scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.
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