Benchmarking GPT-6 Astra vs Opus 5.5 in a digital fly brain circuit
A simulation of the Drosophila mushroom body circuit under two hyperparameter presets found both models equally accurate, with Astra twice as fast and Opus more energy-efficient.
GPT-6 ASTRA VS OPUS 5.5: I RAN BOTH AI ARCHITECTURES INSIDE A DIGITAL FLY BRAIN TO PROVE THE ULTIMATE SPEED VS ACCURACY TRADEOFF What happens when you benchmark GPT-6 Astra against Opus 5.5 inside a biological neural circuit of Drosophila mushroom bodies? The Fly Probe Duel simulation runs the exact same fly brain circuit under two distinct hyperparameter presets to resolve the classic speed vs. precision debate. Key Neural Architecture & Benchmark Breakdown: * Spiking Biological Scaffold: * Circuit Pipeline: 180 input sensory channels → divisive normalization → 720 Kenyon cells with APL inhibition → MBON outputs → drift-diffusion evidence accumulator. * Plasticity & Attention: Dopamine signals convey prediction errors after every trial to update weights, while a ring attractor dynamically guides attention during hidden rule shifts every ~90 trials. * Model Preset Tradeoffs: * GPT-6 Astra ("Fast & Broad"): High learning rate (η = 0.90), 10% active Kenyon cells, low decision threshold (0.22). * Opus 5.5 ("Slow & Sparse"): Moderate learning rate (η = 0.45), ultra-sparse 5% active Kenyon cells, high decision threshold (0.50). * Benchmark Results: * Equal Accuracy: Both architectures reach an identical 82% steady-state accuracy. * Astra Wins on Speed: Responds 2x faster (121 ms vs 258 ms) and relearns significantly faster after rule shifts (72% vs 67%). * Opus 5.5 Wins on Efficiency: Delivers higher precision per answer (87% vs 84%), consumes 40% fewer spikes (2.7k vs 4.4k/s), and rewrites 50% fewer weights. Which trade-off is superior for future Edge AI agents-instant speed or energy-efficient precision? 👇
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