We compare how the three frontier models handle multi-step reasoning, math, and planning tasks, and where each one still trips.
Reasoning benchmarks have become the new battleground because everything else, fluency, general knowledge, basic instruction following, is now table stakes across the frontier tier. Claude Opus 4.5, GPT-5.2, and Gemini 3 Pro all write clean prose and follow simple instructions without issue. What separates them now is what happens when a task requires holding several dependent steps in mind at once without losing the thread.
Multi-step reasoning and planning
Claude Opus 4.5 tends to show its strongest form on tasks that require decomposing a large ambiguous problem into an ordered plan and then executing that plan without drifting off course partway through. In longer agentic-style tasks, ones involving several tool calls or a sequence of dependent decisions, it is noticeably less prone to losing track of the original goal than the other two, and it more reliably flags when a step in its own plan turned out to be wrong rather than quietly pushing forward with a broken assumption.
GPT-5.2 remains extremely strong on structured, well-defined multi-step problems, particularly where the steps are logically deducible from the prompt itself rather than requiring the model to invent its own plan from scratch. Where it occasionally lags Opus 4.5 is on open-ended planning tasks where the right decomposition is not obvious, it can commit to a plan a beat too early rather than considering alternatives first.
Gemini 3 Pro's reasoning shows up most clearly when the task mixes reasoning with large amounts of source material, reasoning over a long document, a big codebase, or several data sources at once. It is competitive on pure abstract reasoning but its real edge is reasoning that has to stay grounded across a huge context window without losing precision, which is a different and arguably harder skill than reasoning over a short, self-contained prompt.
Math and quantitative problem solving
On competition-style math and quantitative reasoning, all three models are close enough that small prompt variations can flip the result, but a few consistent patterns hold. GPT-5.2 is the most reliable on problems that benefit from systematic, almost algorithmic step-by-step decomposition, showing its work in a way that is easy to audit and correct if something goes wrong partway through. Claude Opus 4.5 does comparably well but is somewhat more likely to catch and self-correct its own arithmetic slip mid-solution rather than carrying an early error all the way to a confidently wrong final answer. Gemini 3 Pro performs strongly on quantitative problems embedded in longer real-world contexts, extracting the relevant numbers from a messy document and reasoning over them correctly, more than on isolated abstract math puzzles.
Where each model still trips
None of the three is reliable on adversarial reasoning traps designed specifically to exploit pattern-matching shortcuts, slightly modified versions of famous logic puzzles where the familiar answer is wrong for the new version. All three models will occasionally pattern-match to the well-known answer rather than reasoning through the actual modified problem, though Opus 4.5 catches these traps somewhat more often when explicitly asked to double-check its own reasoning before answering.
Long chains of reasoning that require holding many constraints simultaneously, scheduling problems with a dozen interacting rules, for instance, remain a weak spot for all three, with accuracy degrading as constraint count rises. This is less about which model is smarter and more a structural limit of reasoning in a single forward pass without external verification tools.
What this means for choosing a model
The practical implication is that reasoning quality is now task-dependent rather than a single ranked list. Agentic, multi-step execution work favors Opus 4.5. Structured, auditable step-by-step problem solving favors GPT-5.2. Reasoning that has to stay accurate across huge amounts of source material favors Gemini 3 Pro. Picking one model as a default for every reasoning task and never comparing leaves real accuracy on the table.
Because the gap between them shifts by task and even by specific prompt phrasing, the most reliable approach for anything high-stakes is running the same question through more than one and comparing the reasoning traces, not just the final answers. Vincony.com's Model Comparison tool makes that a two-click check instead of three separate subscriptions and browser tabs.
The reasoning race has clearly not been won outright by any one lab, and that competitive pressure between Anthropic, OpenAI, and Google is precisely what is pushing all three models past their previous limits at a pace that would have looked implausible even two years ago.