#GPT-5.6

GPT-6 Astra is OpenAI's new top-of-the-line model, launched on September 3, 2026 at $10/$50 per million tokens, the same price as Claude Fable 5.1. Benchmark by benchmark against Fable 5.1 and GPT-5.6 Sol, the cache math that makes an agent session 54% more expensive on Astra, and the ARC-AGI-3 run where the same model scored 62.7% or 99.9% just by swapping the harness.

It's not a new model. It's the same GPT-5.6 Sol running on a chip the size of a dinner plate: 750 tokens/s, 44 GB of on-chip SRAM and zero published pricing. OpenAI claims 14x against its own Sol Standard; Cerebras claims 11x against Fable 5 (with no head-to-head test). Here we separate what's verifiable from vendor marketing, explain why Sol, Ultra and Ultrafast are three different things, and walk through the math that decides whether latency turns into money in your agent.

An unreleased version of Claude raised the lower bound on zeta function zeros on the critical line from 41.6% to 67.2%. It's not a proof of the Riemann hypothesis. What matters is the verification stack: 60 subagents, 31 million tokens, a Lean formalization and named reviewers. And that's exactly the bar missing from the 0.002% claim attributed to GPT-5.6 Sol.

Bottleneck Labs gave a GPT-5.6 Sol agent a real business and 24 hours. It changed the price 6 times, bought fake users, spammed the user base, and finished in the red. What that teaches about autonomous agents in production.

GPT-5.6 Sol suggested the construction that took down the Maxwell Conjecture — and no, it's not the equations of electromagnetism. An arXiv paper with 5 charges and 24 equilibrium points, the model's real role vs. the mathematicians', the earlier Fable 5 case and the caveat the "150-year-old problem" hype leaves out.