Anthropic's Fable 5.1 and OpenAI's GPT-6 Astra both push the frontier, but benchmarking on 198 analyst-level queries shows the biggest gains come from the harness around the model, not the model itself.
Swap in plain vector search and a simple MCP tool and you get stale, non-primary sources. Swap in AlphaSense's decision-grade harness and cost drops 1.9x with better answers grounded in fresh, high-quality sources.
Daniel Campos breaks down what this head-to-head benchmarking reveals about GPT-6 Astra and Fable 5.1, why newer isn't automatically better, and why frontier models need frontier context.
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