Gemini 3 is designed to compete at the high end of reasoning benchmarks, and public data from Google suggests that Gemini 3 Pro performs very strongly on math, science, and multi-step reasoning tasks. On many academic and synthetic benchmarks, Gemini 3 scores at or near the top, especially in areas like advanced math competitions, scientific reasoning, and complex planning. That said, both Gemini 3 and GPT-4o are large, capable models, and their performance tends to be close enough that prompt design and system setup often matter more than a small difference in benchmark scores.
For developers, the important question is less “who wins a particular benchmark” and more “how does Gemini 3 behave in real workflows?” Gemini 3’s strength is its combination of long context, multimodal input, and dynamic thinking. This makes it particularly effective for tasks that involve big, messy inputs—like multi-document analysis, codebase understanding, or long-running planning problems. GPT-4o is also strong in reasoning, but if you are building on top of the Gemini ecosystem, it’s usually more practical to optimize your prompts, retrieval, and tools for Gemini 3 rather than chasing small benchmark differences.
When you bring vector databases into the picture, both models benefit heavily from good retrieval design. With Gemini 3, you can build a system where a vector database such asMilvus or Zilliz Cloud. handles large-scale semantic search, while the model focuses on reasoning over the top-k results. This architecture tends to matter more for overall solution quality than the difference between two state-of-the-art models on a leaderboard. In short: Gemini 3 is competitive on reasoning benchmarks and, with good system design, is more than capable of powering demanding reasoning-heavy applications.