Agentic Benchmark

Google DeepMind, in partnership with Artificial Analysis, has launched the Multimodal Knowledge Agent (MKA) benchmark to evaluate agentic knowledge work over complex visual files. It comprises 200 private real-world tasks across 10 industries, requiring agents to produce deliverables such as slide decks, spreadsheets, and reports from dense visual inputs like architectural drawings, engineering schematics, and clinical scans, emphasizing realistic visual complexity and domain specificity.

Meta has launched Muse Ember, its smallest and most efficient model to date, purpose-built for complex reasoning and high-frequency agentic workloads. It matches Claude Haiku 5.5 on the Artificial Analysis Index (36) at one-third the per-task cost, while leading on agentic and multimodal benchmarks such as AutomationBench-AA and OSWorld 2.1. The model is available immediately via Meta Model API, Google Cloud and Oracle Cloud, priced at $0.10/$0.50 per million input/output tokens.