Gemini 3 vs. GPT-5-Codex: The Real Daily Driver Test

The AI coding landscape changes weekly, but my daily driver has remained consistent: Cursor powered by GPT-5-Codex. It’s smart, surgical, and knows my habits. So when Google released Gemini 3, I didn’t care about the marketing fluff. I wanted to know one thing: Can it replace Codex?

I’ve been using the standard Gemini 3 model (not the high-latency reasoning variants) for the last few hours. Here is the honest breakdown.

Speed and Latency: Agent Mode Unleashed

I spend most of my day in Cursor’s Agent Mode, not just tabbing through autocomplete. We’ve accepted that GPT-5-Codex is slow. It’s the price we pay for high-quality, reasoning-dense code generation. The “thinking” pause before the Agent starts planning is palpable.

Gemini 3 destroys it on latency.

It’s not just slightly faster; it feels like a different class of tool. Google’s TPU infrastructure is flexing here. When I ask the Agent to refactor a component, Gemini 3 is streaming the plan before GPT-5-Codex has even finished tokenizing the request. If you care about flow state, this speed difference is jarring—going back to Codex feels like wading through molasses.

The Benchmarks

The “feel” is backed by the numbers. Early benchmarks from around the community are painting a clear picture:

  • HumanEval: Gemini 3 is hitting 94.2%, edging out GPT-5-Codex’s ~92%.
  • Latency (Time to First Token): Gemini 3 is clocking in at ~40ms vs GPT-5-Codex’s ~250ms. That’s a 6x improvement in perceived responsiveness.
  • GPQA Diamond: Gemini 3 scores 91.9%, a solid step up from the GPT-5-series baseline of 88.1%.

While GPT-5-Codex is still arguably more “terse” and architecturally sound for complex backend logic, Gemini 3 is winning on raw throughput and reactivity.

The Meta-Layer: FactoryAI

While I use Cursor for my 9-to-5, I’ve switched to FactoryAI for all my personal projects. In fact, I’m dogfooding it right now: this blog post was researched, drafted, and refined by a Factory Droid running Gemini 3 Pro.

Using Gemini 3 in an autonomous loop (like Factory) highlights the speed advantage even more. When an agent is running a multi-step task—research, plan, edit, verify—saving 2 seconds per inference call compounds into minutes of saved time.

The Verdict

For 90% of my day—writing new components, tweaking CSS, writing tests—I’m seriously considering switching my Cursor default to Gemini 3 purely for the speed. It keeps me in the flow state that GPT-5-Codex often breaks with its sluggishness.

But I still keep a Codex tab open for the heavy, complex logic where I trust its “Senior Engineer” intuition slightly more. We’ll see if Gemini’s speed can win me over completely in a week.