As large language models (LLMs) gain momentum worldwide, there’s a growing need for reliable ways to measure their performance. Benchmarks that evaluate LLM outputs allow developers to track ...

AI development is often framed as a race among countries, companies and academic researchers. But figuring out who’s actually ...

Researchers debut "Humanity’s Last Exam," a benchmark of 2,500 expert-level questions that current AI models are failing.

The rivalry between Qwen 3.5 and Sonnet 4.5 highlights the shifting priorities in large language model development. Qwen 3.5, ...

In updated tests published to the Humanity's Last Exam website, Gemini's 3.1 Pro model achieved 45.9 percent accuracy, with a ...

As large language models (LLMs) continue to improve at coding, the benchmarks used to evaluate their performance are steadily becoming less useful. That's because though many LLMs have similar high ...

MLCommons today released AILuminate, a new benchmark test for evaluating the safety of large language models. Launched in 2020, MLCommons is an industry consortium backed by several dozen tech firms.

MLCommons recently launched AILuminate, the first safety test specifically designed for LLMs. The v1.0 benchmark generates safety grades for widely adopted LLMs and represents a collaborative effort ...

This study introduces MathEval, a comprehensive benchmarking framework designed to systematically evaluate the mathematical reasoning capabilities of large language models (LLMs). Addressing key ...

Alibaba launches Qwen 3.5 AI models with 0.8B to 9B parameters, claiming performance close to much larger chatbots.