Agent gets 2.53x faster and cuts 65% of tokens via JIT-style execution, same 9B model
AggressiveOrder2584 · reddit · 2026-10-24
A developer shares Solar Agent optimization experiments: instead of upgrading the model, redesign how the agent runs using a JIT-inspired execution system that cuts unnecessary LLM calls. Testing three versions on the same Ornith 1.5 9B model (10 runs, 5 tasks x 2):
- Total time: 92.45s → 36.59s (2.53x faster)
- Tokens: 30,492 → 10,601 (65.2% fewer)
- LLM calls: 26 → 10 (61.5% fewer)
- Quality score unchanged at 98.5/100, 10/10 success
Key changes: removing redundant planning steps, deterministic execution of predictable operations, parallelizing independent tool calls, and reserving the full reasoning engine for complex tasks. The GitHub task dropped from 12.76s to 4.15s; Gmail from 9.06s to 3.27s.
The author notes these are controlled benchmarks with real model calls but simulated connectors, with small sample sizes. The takeaway: the biggest opportunity often isn't building a smarter model, but building a smarter system around the model.
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