GPQA Diamond — cost vs performance

Best current row per canonical model in the group “accuracy · variant=GPQA Diamond · evaluator=Artificial Analysis” (459 models) against cheapest current output price (USD / 1M 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 (7 models)bubble = total parameters

Frontier models 7

Pareto frontier
Model	accuracy on GPQA Diamond	Rank	cheapest current output price (USD / 1M tokens)	Provider	Trust
gpt-6-astraOpenAI · Closed	96.3%	1	$25	OpenRouter	Independent
Gemini 3.8 FlashGoogle · Closed	95.3%	2	$1.88	OpenRouter	Independent
MiniMax-M3MiniMax · Open weights	92.9%	14	$1.2	OpenRouter	Independent
Qwen3.8 FlashQwen · Open weights	92.3%	21	$0.47	Together AI	Independent
GLM 5.3 FlashZ.ai (Zhipu AI) · Open weights	91.2%	30	$0.25	Z.ai API	Independent
Ling 3.0 Flash VLinclusionAI · Open weights	86.2%	81	$0.18	OpenRouter	Independent
Ling 3.0 FlashinclusionAI · Open weights	85.5%	91	$0.063	OpenRouter	Independent

Methodology. Points are the best current row per canonical model in comparability group 'accuracy · variant=GPQA Diamond · 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.