GPT-5.5 vs OpenMythos

OpenAI shipped GPT-5.5 on April 23, 2026, positioning it as "a major step toward a unified AI super-app" that combines chat, coding, and browser use in a single model. It is accessible via a new Agent Mode dropdown inside ChatGPT for Pro, Plus, and Team subscribers, and through the API for developers. The model delivers stronger tool use and reliability than its predecessors, with particular improvements in multi-step agentic task completion. New workspace agents for ChatGPT Business and Enterprise can autonomously handle tasks across Slack, Gmail, and other connected platforms — the same territory OpenAI has been building toward since the Agents SDK launch earlier this year. GPT-5.5 is OpenAI's answer to growing pressure from Anthropic's Claude Opus 4.7, Google's Gemini Enterprise platform, and open-source contenders like Kimi K2.6 and Arcee Trinity. Whether it actually leapfrogs the competition or merely matches it is still shaking out in independent benchmarks, but for the millions of existing ChatGPT users, it's the biggest capability jump they'll feel in day-to-day use this year.

OpenMythos is a community-driven theoretical reconstruction of Claude Mythos's suspected architecture, implementing a Recurrent-Depth Transformer (RDT) — a looped transformer that recycles layers multiple times per forward pass for deeper reasoning without massive parameter growth. The project drew 10,100 GitHub stars in its first week, reflecting intense developer curiosity about what's powering Anthropic's latest generation models. The architecture has three stages: a Prelude (initial layers), a Recurrent Block (looped up to 32 times with shared weights), and a Coda (final layers). Rather than stacking hundreds of unique layers, the recurrent block runs the same weights multiple times with learned injection parameters updating hidden states between loops — enabling implicit chain-of-thought reasoning in continuous latent space without generating intermediate tokens. The project supports Grouped Query Attention (GQA) with optional Flash Attention 2, Multi-Latent Attention (MLA), and sparse MoE with routed and shared experts. Model scales range from 1B to 1T parameters. The key claim is that RDT achieves reasoning depth comparable to fixed-depth models with far more parameters, since computational complexity scales with loop iterations rather than layer count. This would explain how Claude Mythos achieves strong reasoning performance without the extreme parameter counts of brute-force scaling — though Anthropic has neither confirmed nor denied the architecture.