Leading language models show different strengths across benchmarks

Language model selection depends on the workload, with different leaders emerging across scientific knowledge, multimodal reasoning and software engineering. GPQA measures performance on expert-written science questions, MMMU-Pro tests reasoning across visual and written inputs, and SWE-bench Verified evaluates fixes to real software repositories. Some models lack public results across all benchmarks, limiting direct comparisons.

Gemini 3.1 Pro leads the general-knowledge selection with 94.3% on GPQA. Gemini 3 Flash and GPT-5.4 each score 81.2% on MMMU-Pro, with the former positioned for fast, high-volume applications and the latter for complex professional analysis. Claude Opus 4.5 leads the coding selection with 80.9% on SWE-bench Verified, followed by Gemini 3.1 Pro at 80.6% and MiniMax M2.5 at 80.2%. GLM-5 offers an open alternative focused on systems engineering and long-running agentic tasks.

Deployment economics can matter as much as benchmark performance. Grok 4 Fast combines 85.7% on GPQA with an input price of $0.25 per million tokens, while Gemini 3 Flash delivers 90.4% at $0.92. Practical evaluation should also consider latency, reliability, multimodal support and context capacity. Testing models on application-specific data remains important before committing to a provider.