BTC and ETH price movements are volatile frequently.

I discovered something - when analyzing the same market issue with AI twice at different times, the judgments weren't completely consistent.
After reviewing the call logs, I found the problem was on my end.
Previously, I routed all requests through the strongest model uniformly, to save effort and felt it was more stable.
This caused higher latency during high-frequency periods, output stability decreased, and calling costs increased significantly.
For powerful models like GPT and Gemini, frequent daily calls aren't cheap, and sometimes the returns don't even cover the costs.
I changed the logic to a tiered structure - simple questions use lightweight models, complex questions use strong models.
Manually maintaining this traffic distribution ruleset is draining, and debugging time exceeded the trading itself.
I started using a unified model entry point, letting the system automatically distribute based on task complexity.
GateRouter launched by Gate enables calling all models with one API, which is a multi-model routing architecture that can automatically select the most suitable model as needed.
Results are more stable, latency decreased, and overall costs dropped significantly.
Struggling over which model to choose,
might as well let the system complete model selection automatically.