Why Claude Opus 5.5 Challenges The Use Of Max By Default In AI

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Anthropic’s latest model, Claude Opus 5.5, launched on September 22, 2026, with a focus on delivering stronger performance and lower operating costs. The release questions the common practice of defaulting to maximum reasoning effort, as higher settings incur significantly higher expenses, despite their performance benefits.

Claude Opus 5.5 achieved the top position on the Artificial Analysis Intelligence Index with a score of 58 at maximum effort, compared to 51 at medium effort. The model’s maximum setting costs approximately $5.98 per task, which is roughly 4.5 times the cost of medium effort at $1.34, yet provides only a 7-point increase in index score. The model excels in professional, agentic knowledge work, outperforming competitors on key evaluations such as AA-Briefcase, where it scores 1,822 Elo points—143 points ahead of Fable 5.1.

Despite performance gains, the cost differential raises questions about the practicality of always using max settings. Anthropic’s data shows that lower effort configurations, like medium, can deliver nearly comparable results at a fraction of the cost, prompting organizations to carefully evaluate their specific task needs before defaulting to the highest setting.

Implications for AI Deployment Costs and Strategy

This development underscores the importance of cost-benefit analysis in AI deployment. While maximum effort settings deliver the highest performance, the significant cost increase may not justify their use for all tasks. Organizations must consider whether the incremental gains outweigh the added expense, especially when lower settings can achieve acceptable results at a fraction of the cost. This challenges the prevailing assumption that defaulting to max is always optimal, urging a more nuanced approach to configuration choices.

Background on AI Effort Settings and Cost Structures

Prior to Claude Opus 5.5, AI models often defaulted to the highest reasoning effort, driven by the belief that maximum performance was necessary for complex tasks. Anthropic’s earlier models and other industry benchmarks demonstrated a trade-off between cost and accuracy, but the recent release explicitly quantifies this relationship. The release also aligns with broader industry trends towards cost-efficient AI, especially as organizations seek to balance performance with operational expenses.

The Artificial Analysis Intelligence Index has become a key metric for evaluating AI models, with scores reflecting reasoning capabilities and practical effectiveness. Claude Opus 5.5’s performance on this index highlights its strengths, particularly in knowledge work, but also emphasizes the importance of carefully selecting effort levels based on task requirements.

Unresolved Questions About Cost-Benefit Optimization

It remains unclear how organizations will implement effort-level testing in practice, and whether the performance gains at maximum effort justify the higher costs across diverse use cases. The precise thresholds for cost-effectiveness may vary significantly depending on task complexity and operational budgets. Additionally, the long-term implications of deploying different effort settings at scale are still being studied, and further data is needed to establish best practices.

Next Steps for Organizations and AI Developers

Organizations are encouraged to conduct their own benchmarking with Claude Opus 5.5 across representative workloads to determine optimal effort settings. Future updates from Anthropic and independent researchers will likely provide more detailed guidance on cost-effective configurations. Additionally, AI developers may introduce more granular control options, enabling users to fine-tune effort levels based on task importance and budget constraints.

Key Questions

Should I always avoid using max effort in AI models?

Not necessarily. While max effort offers the highest performance, it also incurs significantly higher costs. Organizations should evaluate whether the performance gains justify the expense for their specific tasks and consider testing lower effort settings first.

How much more expensive is max effort compared to medium effort?

According to Anthropic’s data, max effort costs roughly 4.5 times more per task than medium effort, with a cost of about $5.98 versus $1.34, respectively.

What metrics should organizations use to decide effort settings?

Key metrics include AI performance on relevant evaluations, cost per task, and the quality of outputs in terms of completeness, correctness, and usability. Benchmarking with actual workloads is recommended.

Does higher token use at max effort negate cost savings from caching?

While max effort models tend to use more tokens, Anthropic reports that caching strategies significantly reduce token costs, making high-effort configurations more feasible in cost-sensitive deployments.

Will future AI models offer more granular effort controls?

It is likely. As organizations demand more cost-effective solutions, AI developers may introduce finer-tuned effort settings to balance performance and expenses more precisely.

Source: ThorstenMeyerAI.com