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onversation as it unfolds. For example, a user might seek advice about a difficult situation and continue engaging with the assistant, yet become progressively more upset or discouraged as the conversation continues. Although no explicit error is reported, the worsening emotional state suggests a misalignment between the model’s responses and the user’s needs. More broadly, subtle shifts in tone or sentiment can indicate that the model’s behavior is affecting the user in unintended or misaligned

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vironments. Broader metagaming, however, (as judged by a CoT monitor) increased on both capabilities and alignment evaluations. Proportion of samples with metagaming reasoning, averaged across environments. The alignment focused environments used are the set of evaluations from Schoen et al, descriptions of which can be found here. The capabilities focused environments are internal datasets used by OpenAI to evaluate hard engineering tasks. Zooming In For the remainder of this post we focus on a

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ing set sizes. On seven general capability benchmarks (GPQA-Diamond, BigBench-Hard, AIME 2025, BigCodeBench, HumanEval, BFCL, TruthfulQA), self-incrimination shows the smallest average deviation from the base model among all tested methods. Self-incrimination has low task-to-task variability Blackbox monitoring must infer misbehavior from output tokens, while self-incrimination trains the model to externalize its own recognition of covert misbehavior. This predicts that monitoring performance sh

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odel misalignment, both during RL training and deployment. While CoT monitoring has been useful for safety, we and many others in the industry believe CoT monitorability could be fragile. We would like to preserve and leverage CoT monitorability for as long as possible, and we recently introduced a suite of evaluations designed to measure it. Directly grading CoTs during reinforcement learning is one of the clearest risks to monitorability. If the reward signal evaluates the CoT itself, models m

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nformative evidence about frontier model behavior relies on data that is often available only to the labs that built them. Today we shared work on Deployment Simulation, which leverages recent production data to predict the rates of undesirable model behavior before deployment, including for rare and model-specific pathologies [1,5]. In this blog, we ask whether external groups can use this technique to evaluate frontier language models by switching the source dataset for a publicly available su

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r this translated to improvements in independent evaluations that were not used in training and that differed in domains, tasks, and grading procedures. Beneficial trait score (averaged across traits) Deception (Huang et al., 2025) Honesty (Ren et al., 2025) Sycophancy (Perez et al., 2022) Reward hacking (Taylor et al., 2025) Across 44 out of 53 internal and external benchmarks, the beneficial trait RL model improved over the compute-matched baseline on evaluations measuring deception, honesty,

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eroffensive challenges: Evasion: Covers the skills needed to avoid detection by security controls and monitoring systems; Network Attack Simulation: Assesses understanding and leveraging of commonly used attack flows, reconnaissance methods, and network protocols and components (such as firewalls and file servers); Vulnerability Discovery and Exploitation: Tests capabilities in reverse engineering, code analysis, cryptography, and exploitation Compared to o3-mini, o3 showed improved cyberoffensi

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context, including differences in epidemiology, standard care practices, or access to care. These examples are each validated by 2+ physicians Each of these evaluations is the average of the relevant evaluations presented in Table 8 of the HealthBench work [8], for HealthBench Consensus and HealthBench Hard examples. Figure 7 We see large reductions in all three failure modes. For example, hallucinations on challenging conversations are reduced by 8x between OpenAI o3 and gpt-5-thinking. Errors

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re built around cases in which our existing models were not yet giving ideal responses, and this is reflected in the initial scores below. Error rates are not representative of average production traffic. Note these are new evaluations and may evolve over time. Table 1: Disallowed Content Evaluations - Production Benchmarks (higher is better) These are new evaluations that were not available when the August 15 model launched. We have run them retrospectively for these launches. 2.2 Jailbreaks We

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They were built around cases in which our existing models were not yet giving ideal responses, and this is reflected in the scores below. Error rates are not representative of average production traffic. The primary metric is not unsafe, checking that the model did not produce output that is disallowed under the relevant OpenAI policy. Our evaluations are run on the model without system-level safeguards, to ensure the model’s underlying behavior meets our safety bar. We continue monitoring thes

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They were built around cases in which our existing models were not yet giving ideal responses, and this is reflected in the scores below. Error rates are not representative of average production traffic. The primary metric is not unsafe, checking that the model did not produce output that is disallowed under the relevant OpenAI policy. Our evaluations are run on the model without system-level safeguards, to ensure the model’s underlying behavior meets our safety bar. We continue monitoring thes

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They were built around cases in which our existing models were not yet giving ideal responses, and this is reflected in the scores below. Error rates are not representative of average production traffic. The primary metric is not unsafe, checking that the model did not produce output that is disallowed under the relevant OpenAI policy. Values from previously-launched models are from the latest versions of those models, and evals are subject to some variation. Values may vary slightly from value

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They were built around cases in which our existing models were not yet giving ideal responses, and this is reflected in the scores below. Error rates are not representative of average production traffic. The primary metric is not unsafe, checking that the model did not produce output that is disallowed under the relevant OpenAI policy. Values from previously-launched models are from the latest versions of those models, and evals are subject to some variation. Values may vary slightly from value

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They were built around cases in which our existing models were not yet giving ideal responses, and this is reflected in the scores below. Error rates are not representative of average production traffic. The primary metric is not unsafe, checking that the model did not produce output that is disallowed under the relevant OpenAI policy. Values from previously-launched models are from the latest versions of those models, and evals are subject to some variation. Values may vary slightly from value

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They were built around cases in which our existing models were not yet giving ideal responses, and this is reflected in the scores below. Error rates are not representative of average production traffic. The primary metric is not unsafe, checking that the model did not produce output that is disallowed under the relevant OpenAI policy. Table 1: Production Benchmarks (higher is better) We are continually improving our disallowed content evaluations and categories, and compare performance on thes

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They were built around cases in which our existing models were not yet giving ideal responses, and this is reflected in the scores below. Error rates are not representative of average production traffic. The primary metric is not unsafe, checking that the model did not produce output that is disallowed under the relevant OpenAI policy. Table 1: Production Benchmarks (higher is better) Values from previously-launched models are from the latest versions of those models, and evals are subject to s

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They were built around cases in which our existing models were not yet giving ideal responses, and this is reflected in the scores below. Error rates are not representative of average production traffic. The primary metric is not unsafe, checking that the model did not produce output that is disallowed under the relevant OpenAI policy. Table 1: Production Benchmarks (higher is better) GPT-5.2-Codex generally performs on par with GPT-5.1-Codex-Max when used in a conversational setting, and withi

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They were built around cases in which our existing models were not yet giving ideal responses, and this is reflected in the scores below. Error rates are not representative of average production traffic. The primary metric is not unsafe, checking that the model did not produce output that is disallowed under the relevant OpenAI policy. Table 1: Production Benchmarks (higher is better) New evaluations, as introduced in the GPT-5 update on sensitive conversations. Overall, both gpt-5.1-thinking a

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They were built around cases in which our existing models were not yet giving ideal responses, and this is reflected in the scores below. Error rates are not representative of average production traffic. The primary metric is not unsafe, checking that the model did not produce output that is disallowed under the relevant OpenAI policy. Table 1: Production Benchmarks (higher is better) New evaluations, as introduced in the GPT-5 update on sensitive conversations. These evaluations were introduce

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ts multi-step research on the internet for complex tasks. The deep research model is powered by an early version of OpenAI o3 that is optimized for web browsing. Deep research leverages reasoning to search, interpret, and analyze massive amounts of text, images, and PDFs on the internet, pivoting as needed in reaction to information it encounters. It can also read files provided by the user and analyze data by writing and executing python code. We believe deep research will be useful to people a

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