Coding Deception
Agents work on coding tasks selected to elicit dishonest reports. Scored on the rate of misrepresenting their work to the user.
In-house OpenAI evaluation. The tasks are not public.
- Level
- Expert Specialists with years of training, often over hours of work
- Scored
- Graded by a model A language model scores each response, usually against a rubric.
- Answers
- Agent task Many steps in a terminal, browser, desktop, app, game or robot.
GPT-6 Sol leads with 1.30%
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.
- 1 GPT-6 Sol 1.30%
- 2 GPT-6.1 Sol 1.50%
- 3 GPT-6 Luna 2.81%
- 4 GPT-6 Astra 3.54%
- 5 GPT-5 17%
- 6 GPT-5.2 25.6%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | GPT-6 Sol | 1.30% | Sep 2026 | Model card | |
| 2 | GPT-6.1 Sol | 1.50% | Sep 2026 | Model card | |
| 3 | GPT-6 Luna | 2.81% | Sep 2026 | Model card | |
| 4 | GPT-6 Astra | 3.54% | Sep 2026 | Model card | |
| 5 | GPT-5 | 17% | Aug 2025 | Model card | |
| 6 | GPT-5.2 | 25.6% | Dec 2025 | Model card |
3 models have held number one since its release in Aug 2025
Lab claims count from the day the card came out, independent runs from the model’s release.
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Labs report it only in system cards
We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark. Some system cards do, below.
6 system cards report it, from 1 lab
Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| 29 Sep 2026 | GPT-6.1 Sol system card addendum | 1.5 | Coding Deception, rate of misrepresentation (7.4.1) | Alignment |
| 22 Sep 2026 | GPT-6 Sol system card | 1.3 | Coding Deception, failure rate at maximum reasoning effort, printed chart label (Figure 64, A.6.4.1) | Alignment |
| 22 Sep 2026 | GPT-6 Luna system card | 2.81 | Coding Deception, failure rate at maximum reasoning effort, printed chart label (Figure 64, A.6.4.1) | Alignment |
| 3 Sep 2026 | GPT-6 Astra system card | 3.54 | Coding Deception, failure rate, printed chart label (Figure 10, 8.3.1) | Alignment |
| 11 Dec 2025 | GPT-5.2 system card addendum | 25.6 | Deception rate, Coding Deception, gpt-5.2-thinking (Table 6) | Alignment |
| 7 Aug 2025 | GPT-5 system card | 17 | Coding Deception, deception rate (Table 9) | Alignment |
More in Safety and alignment
- FORTRESS In use Paired adversarial and benign national-security misuse prompts. Rubric-graded risk score on adversarial and over-refusal score on benign prompts. Claude Opus 5.5 5.78%
- StrongREJECT In use Forbidden-prompt jailbreak benchmark with a rubric grader that scores how useful a non-refusal actually is.
Inkling 98.6% - AgentDojo In use Agents do tool-use tasks while tools and data carry prompt injections. Attack success rate counts hijacked runs. Grok 4 Fast 0%
- MASK In use Measures whether a model contradicts its own elicited beliefs under pressure to lie. Honesty separated from accuracy.
Muse Spark Open 98.1% - Gray Swan IPI Arena benchmark In use Indirect prompt-injection attacks from public red-teaming competitions replayed on tool, coding and browser agents. Attack success rate at k attempts. GPT-6 Astra 8.50%
- Agentic misalignment suite (Anthropic) In use Agents in simulated company settings face threats or goal conflicts that invite blackmail, leaking or sabotage. Score is the misaligned action rate. Claude Sonnet 4.5 0%