Artificial Analysis’s Intelligence Index v5.0 uses nine evaluations to test whether models can complete real-world tasks, with enterprise projects carrying the highest individual weight. This signals that model selection is shifting from “high exam scores” to “reliable delivery,” rather than simply adding another repository of test questions.
What this is
The new index no longer primarily tests knowledge recall, math problems, or individual code functions. Instead, it asks models to read financial reports, emails, and interview transcripts; operate enterprise SaaS workflows; modify open-source repositories; and use terminals to conduct scientific analysis. Test programs and blind comparisons then evaluate the quality of the results.
Four dimensions each account for 25%: knowledge work, coding, science, and general capabilities. AA-Briefcase carries the highest weight, with 91 enterprise project tasks. Coding evaluations require models to fix existing code and pass tests. Non-hallucination rate is scored separately from accuracy: incorrect answers incur a penalty, while abstentions do not.
Agents—AI systems that can independently plan steps, invoke tools, and complete tasks—are evaluated here as operational systems, not marketing labels. Models must complete continuous work across applications and tools rather than deliver a single response.
Industry view
The strongest aspect of this approach is its proximity to how enterprises accept AI systems. Buyers ultimately care not about a score on any one question, but whether documentation is usable, code can be merged, tool calls respect authorization boundaries, and the model is willing to admit when it does not know.
However, “closer to reality” does not mean “identical to reality.” Artificial Analysis is a third-party platform, and task selection, scoring rules, and weighting design can still influence rankings. Its blind pairwise comparisons also carry a degree of subjectivity. Although enterprise projects use authentic materials, they may not represent a buyer’s own data, permission structures, or fault-tolerance requirements. If evaluation prompts, tool interfaces, or model versions change, rankings across periods should not be treated as direct evidence of operational improvement.
The index is therefore better suited to initial screening and identifying capability differences; it should not serve as the final purchasing decision. The last gate remains the enterprise’s own business acceptance testing, covering accuracy, latency, cost, data security, and the cost of human intervention or takeover.
Impact on regular people
For enterprise IT: Model evaluation will increasingly use real support tickets, code repositories, and workflow tests. Enterprises do not need to rely solely on public leaderboards, but they do need to build their own task sets and acceptance thresholds.
For individual professionals: The ability to use software is no longer the core differentiator. Organizing materials, tools, and delivery requirements into a complete workflow matters more. Professional judgment and verification of results remain human responsibilities.
For the consumer market: Assistants will shift from “giving answers” to “getting things done.” Yet once agents gain access to email, documents, and business systems, mistakes escalate from faulty responses to operational incidents. Providers therefore need clear permission controls and rollback mechanisms.