I created an autonomous boxing benchmark [D]

I created an AI boxing match to test the decision speed, adaptability and strategy. I fed the LLMs with data about the current match and if they have vision, they will get even more data. The match has street rules, anything goes and an AI is not defeated until the ref counts to 10 or they do 50% of their HP in damage after being knocked out. I wanted to create a fun benchmark that isn't just boring problems to be solved. Now I test them while stimulating getting punched in the face. I've been testing with gemini-flash-live models because of the speed and vision support it offers. With these models, they can actually dodge punches and counter punches. Local models on my own hardware (5060ti 8gb) take a while to inference so I'm not sure if I should introduce time scaling to compensate otherwise I want to use this to benchmark models so I'm curious on what kind of stats would be useful? Here is what I'm tracking have so far: Speed and Latency Metrics In a real-time fight, a model's speed directly correlates to its "physical" speed. Fast models should attack faster so larger models aren't necessarily going to hit harder. Tokens per Second (TPS) / Throughput: This will help you balance local models against cloud APIs. A model might have a fast TTFT but a slow TPS, meaning its actual action execution takes too long. End-to-End Latency: The total time from when the model receives the snapshot (the prompt) to when the action is executed in the game. This accounts for tool-calling delays. Reaction Latency: Measure the specific delay between an opponent's telegraph (e.g., a heavy punch winding up) and the model's defensive output (e.g., a dodge or block). Action Quality and "Tool" Correctness the model's actions (punching, guarding, taunting) act as tool calls. You need to track how well they use these tools under pressure. Sometimes the model's may not really guard/block so they are typically the ones that find themselves KOd. Tool Correctness / Validity: How often does th