AI Responsibility – OpenAI and Anthropic isn't one shared standard; it's two separate, versioned sets of company policy that happen to get compared as if they were interchangeable. The fastest way to judge either company's claims is to read their actual published framework and the system card for the specific model you're using, not the summary on their homepage.
Short answer: OpenAI's approach centers on its Preparedness Framework (v2, updated April 15, 2025), which tracks biological/chemical, cybersecurity, and AI self-improvement risk. Anthropic's centers on its Responsible Scaling Policy (v3.4, effective July 8, 2026), which sets ASL-2 and ASL-3 safeguard tiers. Read the current version and the model's own system card before trusting either company's marketing page.
I spent about a week doing what most comparisons of these two companies skip: instead of summarizing their blog posts, I pulled up the actual policy documents, checked which version was current, and read the system card for the specific models I use day to day. In my testing, the gap between what gets repeated online and what the documents actually say was bigger than I expected — mostly because both policies have been rewritten multiple times and most write-ups still cite an old version.
OpenAI vs Anthropic: responsibility framework at a glance
| OpenAI | Anthropic | |
|---|---|---|
| Governing document | Preparedness Framework, v2 (April 15, 2025) | Responsible Scaling Policy, v3.4 (July 8, 2026) |
| What it tracks | Biological/chemical, cybersecurity, and AI self-improvement capability thresholds | Capability thresholds for CBRN, AI R&D automation, and misalignment, mapped to ASL-2/ASL-3 safeguards |
| Public model documentation | System cards per release (e.g., the GPT-5.6 system card) | Model Reports and system cards on the Transparency Hub |
| Enforceable rules for users | Usage Policies, updated October 29, 2025 | Usage Policy, paired with per-model RSP evaluations |
| First version published | 2023 (Preparedness Framework v1) | September 2023 (RSP v1.0) |
Both companies revise these documents often enough that the version number matters more than the brand name — treat this table as a starting point, then confirm the current version on each company's own page before you cite it anywhere.
What you'll need
Nothing here is behind a login — every document is public. Open OpenAI's usage policies page and Anthropic's Responsible Scaling Policy in two tabs, then pull up the system card or model report for whichever specific model you're actually using, since the homepage summary and the model-specific document don't always say the same thing. Set aside about 20 minutes for a first pass. Before you start, know your own risk category — health data, minors, financial decisions, or production code all carry different stakes — because that's the question a responsibility comparison needs to answer, not just which company sounds more careful.
Step-by-step: checking AI Responsibility – OpenAI and Anthropic yourself
1. Read the model's system card, not the company's homepage
Skip the product page and go straight to the system card for the model you're actually using. OpenAI publishes these on its Deployment Safety Hub — the GPT-5.6 system card covers what was tested and what limitations shipped anyway. Anthropic publishes the equivalent as Model Reports on its Transparency Hub, with the most recent one dated August 17, 2026. In my testing, the system card for the model I was actually using surfaced limitations that never show up on the marketing page — a homepage sells the tool; the system card is where a company admits its limits.
2. Pin down which policy version you're reading
Both frameworks are versioned, and the version number matters more than most comparisons treat it. OpenAI's Preparedness Framework moved from v1 to v2 on April 15, 2025, narrowing the tracked categories down to biological/chemical, cybersecurity, and AI self-improvement risk. Anthropic's Responsible Scaling Policy has moved further — from v1.0 in September 2023, through a full rewrite at v3.0 in February 2026, to a v3.4 update effective July 8, 2026 that refined its automated R&D threshold. Citing "OpenAI's safety framework" without a version number is like citing a terms-of-service page without a date — it may already be out of date.
3. Check the usage policy separately from the safety framework
The scaling policy governs what a company will train and release; the usage policy governs what you're allowed to do with a model once you have access — two different documents. OpenAI updated its Usage Policies on October 29, 2025 to apply one unified rule set across its products, including new protections for minors. Read the section that covers your actual use case, since a use case that looks fine in a demo can still be restricted once you check the specific policy language.
4. Match the capability thresholds to your own risk, not theirs
Preparedness Framework and RSP thresholds are written for the company's own release decisions, not for your risk tolerance. Anthropic's ASL-2 covers its current baseline safeguards; ASL-3, a materially higher bar involving layered access controls and real-time misuse classifiers, only kicks in once a model crosses a specific capability line. For most everyday business use — writing, coding, customer support — none of this changes what you should do. If you're in a genuinely high-stakes category, it's the exact line to check before you build.
5. Look for outside verification, not just the company's own claims
Every framework here is self-reported by the company that wrote it. Both companies run public vulnerability-disclosure programs that give outside researchers a formal channel to flag problems — OpenAI's runs on Bugcrowd, and Anthropic opened its HackerOne program to the public in May 2026. Neither replaces an independent audit, but an active, funded disclosure program puts more of a company's safety claims in front of outside scrutiny than a policy PDF alone.
6. Write down what it actually means for your use case
Once you've read the real documents, put what you found into two or three plain sentences you'd say to a colleague — not a summary of the policy, but what it means for what you're building. This is the step most comparisons skip, and it's the one that actually matters when you're making a real decision about which model to build on.
Example prompts you can copy
Paste the actual policy text or a link into these — don't rely on a general model's memory of a policy that may have since been revised.
- Plain-English summary: "Summarize this system card and tell me the two limitations that matter most for [describe your use case]: [paste text or link]."
- Usage-policy check: "Based on this usage policy, is this specific use case allowed: [describe what you want to do]? Quote the exact section that answers it."
- Side-by-side read: "Compare these two policy excerpts and tell me what's actually different in practice, not just in wording: [paste both]."
- Vendor questions: "I'm evaluating an AI vendor for [industry/use case]. What should I ask them about their safety practices before I sign a contract?"
In my testing, asking for the exact section that answers a specific question worked far better than asking a model to "summarize the policy" — a general summary tends to smooth over the exceptions that actually decide whether your use case is allowed.
Common mistakes to avoid
The mistake I see most is trusting a company's homepage summary instead of the policy document it's summarizing — homepages are marketing, and marketing rounds up. Close behind it is citing "OpenAI's framework" or "Anthropic's policy" without a version number; I found write-ups online still describing Anthropic's 2023 RSP as current, three rewrites later. A strong safety framework also doesn't mean zero risk for your specific use case — it means the company has a documented process, which is different from a guarantee. Enterprise and API agreements often carry different, sometimes stricter, terms than the consumer product's usage policy, so don't assume the ChatGPT.com or Claude.ai terms automatically cover an API integration. And don't skip rechecking after a model update — a new model can ship under a fresh system card while the old marketing page describing the previous version is still live.
Tools that make this easier
If you're turning this research into a document your team actually follows, my guide on how to write an AI policy for your business covers turning exactly this kind of audit into a two-page policy people read. If the real decision is which company's product to use rather than just their policies, my Claude vs ChatGPT comparison covers where output quality actually differs, and my guides to using Claude and using ChatGPT cover setup once you've picked one. Smaller teams weighing AI adoption against the responsibility question should also see my best AI tools for small business roundup, and how I rate AI tools explains the scoring behind every recommendation on this site.
My take
Neither company's framework is marketing fluff, and neither is a finished product either — both have been rewritten multiple times in the past three years, which is itself a sign the risks they're tracking keep moving. OpenAI's Preparedness Framework reads tighter and more narrowly scoped; Anthropic's RSP reads more elaborate, with more public detail per model. Elaborate isn't automatically better — it's more to verify. The honest takeaway from a week of reading both closely: judge the specific model and the specific policy version in front of you, not the company's reputation for caring about safety in general.
Frequently Asked Questions
Is checking AI Responsibility – OpenAI and Anthropic policies free to do yourself?
Yes. Every document referenced here — the Preparedness Framework, the Responsible Scaling Policy, both usage policies, and both companies' system cards — is published free and publicly, with no account required.
How long does it take to check AI Responsibility – OpenAI and Anthropic claims properly?
About 20 to 30 minutes for a first pass on one model: reading the current policy version, skimming the relevant system card, and checking the usage-policy section for your use case. Going deeper into a specific capability threshold takes longer, but most business use cases don't need that depth.
What's the easiest way to compare OpenAI and Anthropic's safety commitments?
Start with the table in this guide, then confirm the version numbers directly on each company's page, since both policies get revised. Don't compare a summary of one company's policy against a summary of the other's — read both source documents side by side.
Does a strong safety framework mean an AI model is risk-free for my use case?
No. A published framework means the company has a documented risk process and a version history you can check — it isn't a guarantee that a specific use case is safe. High-stakes use cases (health, finance, minors, security) still need their own review regardless of which company's model you use.