Topic
Evaluations that score AI systems on completing multi-step tasks end to end in a real environment, rather than on single-turn answers.
Pine AI's cloud computer for agents resolved 27.4% of SaaS-Bench tasks in Pine's own test, below two rivals at 31.1% and 29.2%. Its higher step score and much lower token bill come from whole systems running different models, so the computer's own contribution has not been measured.
Reality
KaliBench, 8,504 query-command pairs across 1,642 Kali tools, found no open-weight model among 24 configurations topped 42% exact-command accuracy. Scores rise when the model is handed the tool name, but production analysts describe only intent.
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Frontier models average 59.5 on Argo-Bench and clear 95 on only 34.8% of its 210 enterprise data tasks. The benchmark grades the bans and refunds an agent files against a hidden simulator, a step outside what text-to-SQL scores measure.
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US companies are buying the cheapest AI model that can finish a job, the FT reports, with Ramp data pointing to a 41% fall in effective token prices. Investors now have to value AI vendors on cost per completed task, a figure that shifts with the workload.
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V4-Flash-Vision-Exp is live on DeepSeek's API and costs a fraction of Anthropic's price. The vendor's own table shows it trailing on eight of eleven tests, including a 12-point gap on repository work.
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Berkeley's RDI center attacked the step where each benchmark computes its score, and without solving a task its own scorecard reports 100% on five of the eight, about 98% on GAIA and 73% on OSWorld.
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The Finance Agent Benchmark scores agents on recent SEC filings using Google Search and EDGAR access, and its best reported result is OpenAI's o3 at 46.8 percent accuracy and $3.79 a query. A full pass runs about $2,035.
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LiteLLM's own Terminal-Bench run puts a gpt-5.4-mini classifier routing across Haiku, Sonnet and Opus at $14.34 against Opus-5's $19.74 for the same 16 solved tasks. The saving works out at 26 cents a task.
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