Over the next 28 days, each day we’ll either ship one thing that is a clear improvement and relevant for most codex/work users or ship a full reset. Let the improvements begin.

Sonnet 5.5 improves on Sonnet 5 across benchmarks, in some cases dramatically. It’s a faster, lower-cost complement to Claude Opus 5.5, strongest at well-scoped everyday tasks, fixing bugs, and creating polished documents, slides, and spreadsheets.

Introducing SWE-2, our closest model yet to the frontier. On leading evals, it scores on par with recent frontier models – at up to 70% lower cost. We scaled RL to multiple trillions of parameters, with a refined recipe that pushes the Pareto curve on both capabilities & cost.

the fly brain can play doom

We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra. The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.

New in Hermes Agent v0.21.0: Persistent Multi-Gateway Connections for Desktop Hook in any combination of remote gateways at once, whether Hermes Cloud instances or local agents, then see every bot and chat across all of them.

Why would anyone pay $80 for a Codex reset? I could and would just buy either another 5x or 20x sub. $80 is so high its a joke. You flood us with resets, every day. Now expect us to be so dumb and foolish, to buy this? Haha. They will begin to slash our normal limits too. This will become mandatory if you wanna use GPT models, longer than a day.

many people ask when to use a big model (like sol/fable) at low reasoning effort, vs a small model (like luna/sonnet) at high reasoning effort i deliberately forced myself to use all the permutations a lot over the last couple of weeks to build intuition, and i realized the difference is "wisdom" vs "diligence" bigger models are "wiser" they have seen a lot. they remember a lot. they have a lot of expertise across different domains. they have better intuition, can connects the dots, and come up with creative, inspired ideas reasoning effort makes a model more "diligent" it'll assess each option, think through consequences, and figure out edge cases etc more thoroughly if there are 100 paths ahead, diligence makes the model assess every single one without a miss, but it will not make the model realize maybe the best one is to take none of the 100 paths and instead dig a tunnel "wisdom" and "diligence" are orthogonal. and now i get why the models are launched the way they were, and not just a single fable level model with 12 different reasoning levels we're all misled by the way we've been plotting the models with benchmark scores, which are fundamentally flawed because they use a single dimension to measure the model's capability, making us think of model size and reasoning effort as being fungible with each other, while in fact "wisdom" and "diligence" needs to be measured separately i hope the evals eventually catch up and address this. until then, here's my recommendation for how to choose - - if the problem you are trying to solve is something you think requires a genius, use a bigger model - if the problem you are trying to solve is something you think requires a pen and lots of paper, use higher reasoning effort - if it requires a genius sitting down with a pen and lots of paper, tune up both