Martian is releasing an interactive analysis showing that routing across multiple large language models yields 46 percent fewer errors than relying on the single best model, measured across 16 widely used benchmarks including TerminalBench and LiveCodeBench. The launch is aimed at teams building AI products who default to one frontier model per task and want a concrete look at where that choice leaves capability on the table. The underlying argument is that any single benchmark score hides the fact that different models succeed and fail on different slices of work, so a well-chosen team of models outperforms the strongest individual system. The company, based at withmartian.com, has been working on model routing and AI interpretability since 2020, with the position that better routing depends on actually understanding what each model is good at rather than treating them as interchangeable black boxes. Co-founders Shriyash Upadhyay and Etan Ginsberg previously sold NLP companies and conducted LLM research at the University of Pennsylvania. Their earlier work includes RouterBench, a dataset of over 30,000 prompts and responses from 11 different LLMs drawn from standard benchmarks such as MBPP, GSM-8k, Winogrande, Hellaswag, MMLU, and MT-Bench , which grounds the routing claims in reproducible evaluation infrastructure. For founders and operators, the timing matters because model choice has become a recurring line item and a recurring bet, with new frontier releases arriving every few weeks. The interactive breakdown lets a reader see, per benchmark, what each model can do when it is used on the queries it handles best, which is useful whether you plan to adopt a router or just want a sharper mental model of where GPT, Claude, Gemini, and open-source systems actually diverge in practice.