Which 5 agent benchmarks actually matter? Ranking GPT-6 Astra vs Claude Fable 5.1
IndyDevDan · youtube · 2026-09-14
YouTuber IndyDevDan argues composite indexes like Artificial Analysis lose the signal that matters — which model to run for your work — and picks his own Top 5 agent benchmarks to rank GPT-6 Astra, Claude Fable 5.1, and open-weights models.
The five benchmarks:
- Terminal-Bench v4.0: cleanest pure agentic coding benchmark; real container, real harness loop, verifier checks final state.
- APEX Agents: expert-authored investment banking/consulting/law tasks — a proxy for knowledge work outside software engineering.
- AutomationBench: 600+ tasks across finance/HR/marketing/ops, where you must hit objectives WITHOUT tripping guardrails; rankings flip hard when violations count.
- AA-Omniscience: hallucination benchmark with correct/incorrect/partial/NOT ATTEMPTED tiers and zero penalty for saying "I don't know" — one upstream hallucination poisons downstream agent pipelines.
- DeepSWE v1.1: long-horizon software engineering from short, realistic prompts.
Key argument: model choice is a 3D problem — performance, cost, speed. On Terminal-Bench v4.0, Astra and Fable 5.1 score close, but Astra is roughly 4x cheaper per task. He also discards saturated benchmarks (zero information gain) and hunts for variance, where the alpha in model selection lives.
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