AgencyBench arXiv paper (2601.11044) ships comprehensive benchmark — 138 tasks across 32 real-world scenarios evaluating 6 core agentic capabilities in 1M-token contexts
The AgencyBench benchmark (arXiv 2601.11044) provides comprehensive evaluation of autonomous agents in 1M-token real-world contexts — 138 tasks across 32 scenarios spanning 6 core agentic capabilities. The benchmark scope is larger than any of the established 'six benchmarks that matter' (GAIA, SWE-Bench Verified, OSWorld, Tau²-Bench, WebArena, METR HCAST), and the 1M-token context targeting reflects the H1 2026 frontier-model long-context default.
The substantive piece is the benchmark-coverage expansion toward 1M-token contexts as the new evaluation default. The established 2026 benchmark suite was designed for the pre-2026 model-context baseline (~200K-1M tokens). With Llama 4 Scout's 10M-context, DeepSeek V4 / Qwen 3.7 / GLM-5.2 all shipping 1M+ context, and frontier closed-source models matching, evaluation benchmarks need to extend to the longer-context regime. AgencyBench is the first comprehensive benchmark targeting that regime explicitly.
The competitive read against the June 20 'six benchmarks that matter' consolidation is that benchmark suites stratify by context-length regime. For short-context production workloads, the established six remain the procurement standard. For long-context production workloads, AgencyBench and successor benchmarks emerging in H2 2026 will become the relevant evaluation surface. Procurement teams should match benchmark choice to deployment context-length shape.
arXiv — AgencyBench: Benchmarking the Frontiers of Autonomous Agents in 1M-Token Real-World Contexts → · AI Agent Square — AI Agent Benchmarks 2026: Performance, Accuracy & Cost Compared →