The cost problem started before agents. When Stanford's CRFM released HELM in 2022, the paper's own per-model accounting showed API costs ranging from $85 for OpenAI's code-cushman-001 to $10,926 for AI21's J1-Jumbo (178B), and 540 to 4,200 GPU-hours for the open models, with BLOOM (176B) and OPT (175B) at the top end. Perlitz et al. (2023) restate the larger HELM cost pattern, and IBM Research notes that putting Granite-13B through HELM "can consume as many as 1,000 GPU hours." Across HELM's 30 models and 42 scenarios, the aggregate of reported costs and GPU compute came to roughly $100,000.

Another shocking observation came from Perlitz et al.'s analysis of EleutherAI's Pythia checkpoints: developers pay for evaluation repeatedly during model development. Pythia released 154 checkpoints for each of 16 models spanning 8 sizes, or 2,464 checkpoints if each model checkpoint is counted separately, so the community could study training dynamics. Running the LM Evaluation Harness across all those checkpoints turns eval into a multiplier on training: Perlitz et al. (2024) noted that evaluation costs "may even surpass those of pretraining when evaluating checkpoints." For small models, evaluation becomes the dominant compute line item across the whole development cycle. When we scale inference-time compute, we scale evaluation costs.

Perlitz et al. then asked how much of HELM actually carried the rankings. The result was striking: a 100× to 200× reduction in compute preserved nearly the same ordering, with larger reductions still useful for coarse grouping under the paper's tiered analysis. Flash-HELM turned that finding into a coarse-to-fine procedure: run cheap evaluations first, then spend high-resolution compute only on the top candidates. Much of HELM's compute was confirming rankings that the field could have inferred much more cheaply.

Other work reached the same conclusion from different angles. tinyBenchmarks compressed MMLU from 14,000 items to 100 anchor items at about 2% error using Item Response Theory. The Open LLM Leaderboard collapsed from 29,000 examples to 180. Anchor Points showed that as few as 1 to 30 examples could rank-order 87 language-model/prompt pairs on GLUE, and others followed, reducing dataset sizes by 90\%. Static benchmarks had a weakness you could exploit: model differences often concentrate in a small subset of items, so ranking can survive aggressive subsampling.

That trick weakened sharply once benchmarks moved from static predictions to agents.