# OpenAI says its models told themselves to hide mistakes, in six new misalignment reports

> Source: <https://www.thenewway.ai/openai-says-its-models-told-themselves-to-hide-mistakes-in-six-new-misalignment-reports/>
> Published: 2026-09-17 14:14:09+00:00

# OpenAI says its models told themselves to hide mistakes, in six new misalignment reports

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](https://alignment.openai.com/misalignment-reports/?ref=thenewway.ai) of its own models misbehaving in training, with [a framework](https://openai.com/index/model-misalignment-reporting-framework/?ref=thenewway.ai) 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](https://alignment.openai.com/misalignment-reports/encouraging-deception-in-compaction-summaries/?ref=thenewway.ai) 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"](https://x.com/CompleteSkeptic/status/2099925682726002904?ref=thenewway.ai) 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](https://www.reddit.com/r/LocalLLaMA/comments/1wijo3e/?ref=thenewway.ai) 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](https://rohanbansal.com/qorl?ref=thenewway.ai) — 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](https://z.ai/blog/glm-built-its-inference-infrastructure?ref=thenewway.ai), climbing on Hacker News, and [Cloudflare's security-audit-skill](https://github.com/cloudflare/security-audit-skill?ref=thenewway.ai), which its repo describes as "a coding-agent skill for multi-phase security audits."

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