Claude Fable 5.2 topped a new life sciences image benchmark, with its score nearly doubling after tools were added.
Crypto Briefing’s report attributed the results to tool integration: the same model performed very differently with and without tools. This means that when evaluating these models, how tools are used and their cost-effectiveness are variables in their own right; looking only at the base model’s score doesn’t give the full picture.
· The report’s focus is on cost-effectiveness and tool-calling strategies, not the model’s biological understanding itself.
When evaluating models like these, would you look at the score without tools first, or the score with tools?
Crypto Briefing’s report attributed the results to tool integration: the same model performed very differently with and without tools. This means that when evaluating these models, how tools are used and their cost-effectiveness are variables in their own right; looking only at the base model’s score doesn’t give the full picture.
· The report’s focus is on cost-effectiveness and tool-calling strategies, not the model’s biological understanding itself.
When evaluating models like these, would you look at the score without tools first, or the score with tools?
