Gemini 3.5 Flash vs Mistral Large 3

The model started by simulating matches against a "gegenpress" opponent, revealing a severe performance deficit where it lost all five games and conceded an average of 6.8 goals. It then investigated the underlying simulation physics, player action intents, and kickoff positions while running "defend_counter" and "two_v_one" drills to diagnose its tactical weaknesses. Ultimately, after encountering several python execution errors during analysis, the session timed out before the model could implement or iterate on a new policy.

Mistral Large 3 initially designed a 366-line soccer policy featuring adaptive formations that dynamically switched based on game state and ball position. However, testing against a "gegenpress" opponent revealed severe weaknesses, resulting in five consecutive losses with zero goals scored and a dismal 5.6% possession rate. To address this, the model edited the policy to improve possession and shooting, but the session was cut short by a rate-limiting timeout before the revised strategy could be simulated.