Study Confirms: Asking LLMs for Concise Outputs Actually Saves Money
ibubbles34 · reddit · 2026-08-22
A study tested cost and accuracy across 9 models by comparing input prompt compression vs. instructing models to be concise.
- Output Compression: Saves 1.5x cost on average (up to 3x), with similar accuracy. Works across languages too.
- Input Compression: Increases costs by up to 96% as models generate longer outputs to compensate, and accuracy drops.
- The findings suggest controlling output length via API prompts is a viable cost-saving strategy since output tokens are typically more expensive than input tokens.
More from Research
- Equilibrium Forcing: Adaptive video generation without noise conditioning — kwangmoo_yi · 2026-08-22
- Equilibrium Forcing Enables Adaptive Video Generation Without Noise Conditioning — kwangmoo_yi · 2026-08-22
- Deep Dive: How World Models Unlock Physical AI and Robotics — risingodegua · 2026-08-22
- OpenBind Released: Small Molecule Co-folding Model Based on OpenFold3 — MoAlQuraishi · 2026-08-22
- Research Uses LLM Code Gen to Drive Evolutionary Algorithms — kenneth0stanley · 2026-08-22
- Yukon Leaderboard Updated to Rank by Total Speedup Contribution — morgymcg · 2026-08-22