Figure 1: Phi-4-reasoning-vision-15B presents a compelling option compared to existing models, pushing the pareto-frontier of the tradeoff between accuracy and compute costs. We have competitive performance to much slower models that require more time and tokens and higher accuracy than similarly fast models. These values were computed by averaging accuracy, time, and output token-counts for a subset of 4 benchmarks: ChartQA_TEST, MathVista_MINI, MMMU_VAL, and ScreenSpot_v2, where we had logged these values.
Even with my foot flat on the accelerator, I was able to navigate tight turns on glare surfaces without having to deploy any fancy ice driving techniques. However, as someone who enjoys deploying such techniques, I was also invited to turn off the car's stability systems and have a little fun.
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