BTC and ETH price action is volatile frequently.

I discovered something: analyzing the same market issue with AI twice at different times produced inconsistent judgments.

After reviewing the call logs, I found the problem was on my end.

I previously routed all requests through the strongest model to save effort and ensure stability, but this caused higher latency during peak hours, reduced output consistency, and significantly increased calling costs.

For powerful models like GPT and Gemini, frequent daily calls aren't cheap, and sometimes the profits don't even cover the costs.

I changed the logic to a layered structure: simple questions use lightweight models, complex questions use strong models.

But manually maintaining this traffic distribution ruleset consumed a lot of energy, and debugging took longer than actual trading.

I started using a unified model entry point, letting the system automatically route based on task complexity.

Gate launched GateRouter, which allows one API call to access all models—a multi-model routing structure that automatically selects the most suitable model based on needs.

The results are more stable, latency is lower, and overall costs dropped significantly.

Rather than agonizing over which model to choose, let the system handle model selection automatically.