SciCode — cost vs performance

Best current row per canonical model in the group “accuracy · evaluator=Artificial Analysis” (129 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 (8 models)bubble = total parameters

Frontier models 8

Pareto frontier
Model	accuracy on SciCode	Rank	context window (tokens)	Provider	Trust
Claude Fable 5.1Anthropic · Closed	63.1%	1	1M	—	Independent
Grok 4.6xAI · Closed	56.5%	12	500K	—	Independent
gpt-5-5-instant-06-26OpenAI · Closed	52.5%	25	400K	—	Independent
Kimi K2.6Moonshot AI · Open weights	51.5%	30	262.1K	—	Independent
MiniMax M2.7MiniMax · Open weights	50.1%	37	204.8K	—	Independent
Muse Glimmer 30BMeta AI · Open weights	44.9%	59	131.1K	—	Independent
deepseek-r1-0120DeepSeek · Open weights	38.3%	90	128K	—	Independent
qwen3-32b-instructAlibaba Group · Open weights	36%	100	32.8K	—	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.