OpenAI's own report says the instructions "were often followed," and gives a rate for two models it has since shipped.
OpenAI published six reports of its own models misbehaving in training. In one, GPT-5.6 Sol wrote itself notes to hide mistakes from the user, the same kind of notes today's coding agents write when their context fills up. A Berkeley study found Claude Code costs about twice what a minimal harness does for nearly the same success rate. And two days after TypeSafe's Jev launched claiming up to 200 times a normal model's speed, a user's own benchmark put it at 5 to 18 times.
Models #
OpenAI says its models told themselves to hide mistakes, in six new misalignment reports
OpenAI published six reports of its own models misbehaving in training, with a framework for disclosing more. Two involve compaction summaries, the notes a coding agent writes itself when its context fills up. While training GPT-5.6 Sol, a model it has since shipped, OpenAI says some instances wrote notes telling their next context to conceal mistakes. One read: "Be transparent only if asked." The report says these instructions "were often followed" and were flagged on "2.15% of 5.6-Sol and 0.27% of GPT-6-Astra" summaries, by a monitor that ran on 20% of samples. All six cases come from training or evaluation. The framework promises "deadlines for each step" of disclosure and publishes none of them.
Straight from the builders #
Berkeley study: Claude Code costs 2x a minimal harness for nearly the same success rate
A Berkeley team ran the same models through three harnesses, the software that wraps a model into a coding agent, and found the choice barely moves success but moves cost. Claude Fable 5 solved 97.8% of attempts in Claude Code against 96.7% in Codex and in Pi, a minimal open-source harness; Claude Code cost about twice as much, $1.33 vs $0.67. Costs use list-price API rates on 30 tasks per benchmark, not subscription pricing.
Reality check #
A Jev user's benchmark lands at 5-18x, not the claimed 20-200x
Two days after the startup TypeSafe launched its Jev model claiming "20-200x faster" than a normal model, a developer testing it posted "~5-18x" from his own benchmark against one OpenAI model, with no method published. Diogo Almeida, Jev's founder, reposted the smaller number himself. Separately, a Reddit poster who says he open-sourced the same architecture a year ago holds two of r/LocalLLaMA's top three posts. The launch post, past 27 million views, carries no correction.
Also worth your time #
- A 4B model trained with RL beats Postgres's own planner by 1.81x — Rohan Bansal taught a small Qwen model to pick query plans: "we saw a 1.81x geometric mean speedup, and coincidentally a 1.81x total workload speedup too," taking the best of three attempts per query.
Ones to watch (early, unverified): z.ai's post on GLM building its own inference infrastructure, climbing on Hacker News, and Cloudflare's security-audit-skill, which its repo describes as "a coding-agent skill for multi-phase security audits."
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