A detailed technical comparison of GPT-5.4 and Gemini 3.1 Pro across OSWorld, ARC-AGI-2, and SWE-bench. See which model leads the race to AGI.
What These Benchmarks Actually Measure
OSWorld, ARC-AGI-2, and SWE-bench stress different skills, so a single “winner” label is usually misleading. OSWorld evaluates whether a model can operate a real desktop environment: open apps, navigate UI state, recover from mistakes, and finish multi-step tasks without a human babysitter. ARC-AGI-2 focuses on abstract reasoning under novelty—patterns and rules the model has not simply memorized. SWE-bench targets software engineering in the wild: reading a repository, locating the right files, writing a fix, and keeping tests green.
When you compare GPT-5.4 and Gemini 3.1 Pro, score the models by task family, not by brand preference. A model that is strong at long-horizon computer use may still be weaker at pure abstraction, and a strong code model may still struggle when the interface is pixels and windows instead of a git tree.
How to Read a Head-to-Head Fairly
Benchmark leaderboards compress messy behavior into one number. That number is useful only if you know the harness: tool access, time limits, whether the agent can retry, how failures are scored, and whether the evaluation matches your stack. Two models can look close overall while diverging sharply on failure modes—one may fail by looping on UI steps; the other may fail by proposing elegant but untested patches.
- Prefer task-level breakdowns over a single aggregate rank.
- Check whether the evaluation allows tools, browsing, or multi-turn recovery.
- Separate “can solve it once” from “solves it reliably under retries and noise.”
- Map each benchmark axis to a job you actually need: desktop automation, novel problem solving, or production code changes.
Practical Tradeoffs for Builders
If your product depends on agents that click, type, and move between applications, weight OSWorld-style results more heavily and test with your real UI latency, permissions, and error dialogs. If you care about general problem-solving under distribution shift, ARC-AGI-2 style tasks are a better stress test than chat demos. If the work is repository-scale engineering—issue triage, multi-file edits, regression safety—SWE-bench style evaluation is closer to daily developer value.
In practice, teams rarely need one model to dominate every axis. Route traffic: use the stronger computer-use agent for desktop workflows, the stronger reasoning model for hard planning steps, and the stronger coding model for pull-request style work. Keep a shared evaluation set of your own tasks so leaderboard shifts do not force blind model swaps.
A Simple Evaluation Protocol You Can Reuse
Pick a small fixed suite: a handful of desktop flows, a handful of novel reasoning puzzles you wrote yourself, and a handful of real bugs from your codebase. Run both GPT-5.4 and Gemini 3.1 Pro under identical prompts, tools, and time budgets. Score success, partial progress, and cost of recovery (how many human intervenes). Log where each model stalls—wrong UI target, brittle plan, or incorrect patch—so the next iteration improves the agent loop, not just the model string in config.
The race toward broader capability is not decided by one chart. It is decided by whether the model’s strengths line up with the jobs you automate, and whether your harness turns those strengths into reliable outcomes. Use OSWorld, ARC-AGI-2, and SWE-bench as complementary lenses, then validate on your own workload before you commit traffic or rewrite your stack around either model.