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. 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.

HarnessTax: How Much Does the Harness Matter for Coding Agents?

What does a coding-agent harness actually add, and at what cost? It turns out your Claude models may not need Claude Code… We evaluate 21 model–harness pairs spanning seven models and three harnesses—Claude Code, Codex CLI, and Pi—on SWE-bench Lite and Terminal-Bench 2.0.

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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