Google DeepMind and Artificial Analysis Launch Multimodal Knowledge Agent Benchmark to Evaluate AI on Complex Visual Workflows

Google's DeepMind team, in partnership with international AI evaluation firm Artificial Analysis, launched the "Multimodal Knowledge Agent" (MKA) benchmark on November 10, 2026. This new standard aims to evaluate how well AI agents perform real-world knowledge work tasks that require reasoning across visually complex files.

Unlike previous benchmarks that relied primarily on text-only inputs, MKA targets the visual nature of everyday professional work, such as cross-referencing architectural blueprints, tracing engineering schematics, and synthesizing findings into polished decks or reports. To ground the benchmark in reality, Google collaborated with domain experts across 10 verticals and 83 occupations to create a 200-task evaluation set. Each task simulates a multi-step project that takes a seasoned professional three to eight hours to complete, paired with an expert-written rubric of roughly 70 yes/no checks, weighted 75% on factual accuracy and 25% on visual design.

To ensure integrity, Artificial Analysis runs the 200-task evaluation set privately, preventing task leakage into training data or discovery via search. A separate set of 10 public tasks has been released on Hugging Face for developers to inspect the format and test their own agents. All models run within the same open-source agent harness, Stirrup, using identical tools, sandboxes, and turn budgets.

Google claims that its latest model, Gemini 4 Carbon, ranks second in quality among tested models on this benchmark, while completing tasks at six times lower average cost compared to the top-scoring model. These results place Gemini 4 Carbon on the Pareto frontier of quality and price for MKA evaluations. However, these performance metrics are currently based on vendor self-testing and have not yet been independently reproduced by third parties.