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OpenAI and Anthropic both publish capability-based safety policies, but they organize them differently. OpenAI’s Preparedness Framework defines selected risk categories, High and Critical capability thresholds, and a review process involving its Safety Advisory Group. Anthropic’s Responsible Scaling Policy (RSP) connects capability thresholds with safeguards, public Risk Reports, and a dated Frontier Safety Roadmap. Their documents support a comparison of what each says it measures, what can trigger action, and what it discloses—not a reliable verdict that one company is safer overall.
How the published approaches compare
| Dimension | OpenAI | Anthropic |
|---|---|---|
| Main policy structure | Preparedness Framework for severe risks from advanced capabilities; a separate Frontier Governance Framework addresses governance and regulatory context. | Responsible Scaling Policy, supported by Frontier Safety Roadmaps and Risk Reports. |
| Triggers | High and Critical capability levels, with different safeguard expectations. | Capability thresholds linked to safeguards; the policy acknowledges that deciding whether some thresholds have been crossed can be subjective. |
| Evaluation and review | Automated evaluations and expert-led deep dives, with Safety Advisory Group review and recommendations. | Assessments and Risk Reports under the RSP; the policy’s change history describes external review provisions for reports. |
| Public updates | Preparedness findings intended to accompany frontier-model releases, plus framework announcements and model-level disclosures. | A public policy change history, roadmap revisions, and Risk Reports; the RSP describes redactions in public reporting. |
The policies use different labels and do not define perfectly matching categories. The OpenAI details here reflect its April 15, 2025 Preparedness Framework update and its May 28, 2026 Frontier Governance Framework announcement. Anthropic’s RSP is a living page, so its version history matters when interpreting the comparison.
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What OpenAI’s Preparedness Framework covers
OpenAI describes the Preparedness Framework as a way to track and prepare for advanced capabilities that could cause severe harm. In the 2025 update, it names biological and chemical capabilities, cybersecurity, and AI self-improvement as tracked categories. It lists long-range autonomy, sandbagging, autonomous replication and adaptation, undermining safeguards, and nuclear and radiological capabilities as research categories in that version. The update says persuasion risks are handled outside the Preparedness Framework.
OpenAI says prioritized risks should be plausible, measurable, severe, net new, and instantaneous or irremediable. Those selection criteria help explain why the framework is not a complete inventory of every possible AI risk: it focuses on risks that meet its stated criteria.
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What High and Critical mean
OpenAI’s High level concerns capability that could amplify existing pathways to severe harm. For covered systems at that level, the company says safeguards must sufficiently minimize the associated risk before deployment. Its Critical level concerns capabilities that could introduce unprecedented new pathways to severe harm; OpenAI says systems at that level need safeguards during development as well as before deployment.
The levels therefore describe different points of intervention, not simply two grades on a general safety scale. The framework’s commitments apply to covered systems and risks; they should not be read as a claim that every risk is measured or eliminated.
How OpenAI says decisions are reviewed
The 2025 update describes a growing suite of automated evaluations alongside expert-led “deep dives.” OpenAI says its cross-functional Safety Advisory Group reviews capabilities and safeguards, assesses residual risk, and makes recommendations that can range from approval to further evaluation or stronger protections. OpenAI Leadership makes the final decisions.
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OpenAI also introduced Safeguards Reports alongside Capabilities Reports. The company says the Safety Advisory Group reviews both and recommends whether deployment is safe enough. OpenAI says it intends to publish Preparedness findings with frontier-model releases; that is a stated practice, not a guarantee that every system’s full internal safety evidence will be public.
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How Anthropic’s RSP works
Anthropic’s RSP is a living policy with a public change history. The history identifies version 3.0, dated February 24, 2026, as a comprehensive rewrite. It describes companion Frontier Safety Roadmaps with detailed safety goals and Risk Reports intended to quantify risk across deployed models. Later 2026 entries describe changes to thresholds, off-cycle model updates, internal sharing requirements, external review provisions for Risk Reports, and redaction indications in public reports. The current policy and its history are available on Anthropic’s Responsible Scaling Policy page.
Thresholds require judgment
The RSP links capability thresholds to safeguards, but a threshold does not make capability measurement automatic. Anthropic’s policy says assessments of whether certain thresholds have been crossed can be subjective. Readers should therefore distinguish a formal trigger rule from the evaluation judgment used to decide whether the trigger applies.
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The live policy also discusses an AI R&D capability threshold and Anthropic’s commitment to publish sabotage-risk reporting for future frontier models clearly exceeding Claude Opus 4.5’s capabilities. That is a forward-looking reporting commitment described by Anthropic, not a claim that such a report already exists for every relevant model.
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Anthropic’s Frontier Safety Roadmap gives dated goals and revision notes, including changes in priorities and target dates for data-retention work and “Moonshot R&D” security projects. The page describes exploring isolated-network workflows and developing a prototype for provable inference by September 30, 2026. These are announced goals; the published material cited here does not establish that they were completed by that date.
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The 2025 cross-lab pilot
In a pilot reported on August 27, 2025, OpenAI and Anthropic each ran their internal safety and misalignment evaluations on the other company’s publicly released models. The exercise examined instruction hierarchy, jailbreak resistance, hallucination, and scheming. OpenAI reported that Claude 4 models generally performed well on instruction-hierarchy tests; jailbreak results were more mixed relative to OpenAI o3 and o4-mini; and, in the tested hallucination setting, the models had high refusal rates and low accuracy on examples they answered. The report also described differing scheming results among the tested models. These are findings reported for that exercise, not a ranking of the companies or their current models. Read OpenAI’s account of the evaluation.
The report cautions that its difficult tests should not be interpreted as directly representative of real-world misbehavior. Results may depend on test design, graders, settings such as whether reasoning is enabled, and the model version. The pilot is useful evidence that the companies tested one another’s models and a window into the behaviors they chose to examine; it is not a controlled comparison of their entire safety programs.
Model disclosures have their own scope
OpenAI’s GPT-5.5 System Card provides a separate example of model-level disclosure: it says GPT-5.5 underwent predeployment safety evaluations, Preparedness Framework evaluation, and targeted red teaming for advanced cybersecurity and biology capabilities. It specifies that results generally describe offline evaluations and that GPT-5.5 results are usually treated as proxies for GPT-5.5 Pro, with exceptions. This illustrates the scope and caveats of that system card; it is not a like-for-like comparison with Anthropic model cards.
How to judge which approach is stronger
There is no common, independently validated score in these materials for overall company safety. A more useful comparison is to ask whether the public mechanism addresses the risk you care about and whether its trigger, decision process, and evidence are clear enough to assess.
- Scope: Check which harms are formal tracked categories, which are still research areas, and which are handled under a different policy. Do not assume that similar-sounding categories have the same boundaries.
- Trigger and response: Look for what capability or risk level prompts additional protections, whether those protections apply during development, before deployment, or both, and how the company says it assesses the trigger.
- Decision authority: Separate who evaluates evidence, who advises, and who makes the final decision. The named bodies and authority described by one company should not be presumed equivalent to the other’s.
- Evidence and disclosure: Distinguish policy commitments from reported evaluations, model-specific findings, and independently verified real-world outcomes. Public reports can aid scrutiny without revealing every internal detail.
- Change over time: Read version histories and roadmap revisions alongside the policy itself. An evolving framework can respond to new capabilities and requirements, but announced goals are not proof of completed work.
OpenAI’s May 2026 Frontier Governance Framework announcement says the Preparedness Framework remains the foundation for managing the most serious risks, while the newer document addresses relevant parts of the approach in the context of emerging legal requirements. It names areas including cyber offense, CBRN risks, harmful manipulation, loss of control, model reporting, security risk management, incident response, external expert input, and framework updates. That distinction helps keep a frontier-capability policy separate from a governance document; neither announcement by itself demonstrates how effective the safeguards are in practice.
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