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AI Atlas

τ²-bench — cost vs performance

Best current row per canonical model in the group “pass^1 · evaluator=Artificial Analysis” (323 models) against cheapest current input price (USD / 1M tokens). The dashed line is the Pareto frontier: no model is both better and cheaper than a point on it.

XOutput priceInput priceParametersContextMemory (est.)ScaleloglinearBubblecontextparamsnone
Group

open / restricted weights closed Pareto frontier (6 models)bubble = context window

Frontier models 6

Pareto frontier
Modelpass^1 on τ²-benchRankcheapest current input price (USD / 1M tokens)ProviderTrust
Z.ai GLM 5.2Z.ai (Zhipu AI) · Open weights99.1%1$0.60OpenRouterIndependent
GLM 4.7 FlashZ.ai (Zhipu AI) · Open weights98.8%3$0.06OpenRouterIndependent
gpt-oss-120bOpenAI · Open weights65.8%122$0.037OpenRouterIndependent
gpt-oss-20bOpenAI · Open weights60.2%131$0.03OpenRouterIndependent
gpt-5-nanoOpenAI · Closed36.5%170$0.025OpenRouterIndependent
Granite 4.0 MicroIBM · Open weights12.6%282$0.017OpenRouterIndependent

Methodology. Points are the best current row per canonical model in comparability group 'pass^1 · 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.