Leading LLMs show different strengths in reasoning and coding

LLM performance varies across scientific knowledge, multimodal reasoning and software engineering, making task-specific evaluation more useful than a single overall ranking. Gemini 3.1 Pro scores 94.3% on GPQA, which tests difficult scientific questions. Gemini 3 Flash and GPT-5.4 each score 81.2% on MMMU-Pro, which evaluates reasoning across images, diagrams and written questions.

For software engineering, Claude Opus 4.5 records 80.9% on SWE-bench Verified, ahead of Gemini 3.1 Pro at 80.6% and MiniMax M2.5 at 80.2%. The benchmark assesses whether models can understand bugs and produce correct patches in real GitHub repositories. Beyond scores, Claude targets long-running engineering work, Gemini supports large codebases and multimodal development, and MiniMax combines programming with planning, search and tool use. GLM-5 offers an open-model option for systems engineering and extended agentic tasks.

Production decisions also depend on cost, response speed, reliability and context capacity. Gemini 3 Flash pairs a GPQA score of 90.4% with a listed cost of $0.92 per million input tokens. Missing public benchmark results limit some comparisons. Teams should test models on their own data and weigh document, image and video support against deployment requirements before choosing a provider.