IFBench — cost vs performance

Best current row per canonical model in the group “accuracy · evaluator=Artificial Analysis” (334 models) against context window (tokens). The dashed line is the Pareto frontier: no model is both better and cheaper than a point on it.

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XOutput price Input price Parameters Context Memory (est.)Scalelog linearBubblecontext params none

open / restricted weights closed Pareto frontier (11 models)bubble = context window

Frontier models 11

Pareto frontier
Model	accuracy on IFBench	Rank	context window (tokens)	Provider	Trust
Grok 4.3xAI · Closed	83.3%	1	1M	—	Independent
Nemotron 3 UltraNVIDIA · Open weights	81.4%	4	262.1K	—	Independent
nova-2-0-proAmazon Web Services · Closed	79.6%	9	256K	—	Independent
GLM 5.1Z.ai (Zhipu AI) · Open weights	76.3%	21	204.8K	—	Independent
command-a-plusCohere · Open weights	74.0%	35	192K	—	Independent
Solar Pro 3Upstage · Closed	71.2%	56	131.1K	—	Independent
Mercury 2Inception · Closed	69.8%	65	128K	—	Independent
olmo-3-1-32b-instructAllen Institute for AI · Open weights	66.0%	87	65.5K	—	Independent
LFM2.5-8B-A1BLiquid AI · Open weights	55.6%	123	32.8K	—	Independent
tri-21b-think-v0-5Trillion Labs · Open weights	54.6%	126	32K	—	Independent
olmo-2-32bAllen Institute for AI · Open weights	38.1%	216	4.1K	—	Independent

Methodology. Points are the best current row per canonical model in comparability group 'accuracy · evaluator=Artificial Analysis'. Price = cheapest current offer across providers (the provider shown). Pareto frontier maximises the score and minimises x; exact ties are all kept. memory_estimate is an estimate (see /methodology). Only the selected comparability group is plotted; points under other configurations are not mixed in. Price = cheapest current offer across providers at the time of the last crawl. Nothing is estimated except where marked.