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SWE-bench Multilingual — cost vs performance

Best current row per canonical model in the group “resolved · board=Multilingual · system=mini-SWE-agent” (13 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.

open / restricted weights closed Pareto frontier (5 models)bubble = context window

Frontier models 5

Pareto frontier
Modelresolved on SWE-bench MultilingualRankcheapest current input price (USD / 1M tokens)ProviderTrust
Claude Opus 4.6Anthropic · Closed72%2$2.5OpenRouterOfficial board
GLM 5Z.ai (Zhipu AI) · Open weights69.7%4$0.60OpenRouterOfficial board
MiniMax M2.5MiniMax · Open weights68.3%6$0.27OpenRouterOfficial board
DeepSeek V3.2DeepSeek · Open weights59%12$0.269DeepSeek APIOfficial board
gpt-5-miniOpenAI · Closed39.7%13$0.125OpenRouterOfficial board

Methodology. Points are the best current row per canonical model in comparability group 'resolved · board=Multilingual · system=mini-SWE-agent'. 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.