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Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →The most useful way to compare AI safety approaches is not to ask which laboratory has the best-sounding policy. Map each framework against the same questions: which threats it covers, how capabilities are evaluated and thresholds set, what protections are required, who can delay or stop a decision, what evidence is disclosed, and how quickly the rules change. Then keep four kinds of evidence separate: policy commitments, results the lab reports, independent assessment, and observed safety outcomes.
The current documents show substantial shared structure but different lifecycle coverage and decision mechanisms. Anthropic lists Responsible Scaling Policy (RSP) version 3.4, effective July 8, 2026; Google DeepMind lists Frontier Safety Framework (FSF) version 3.1, dated April 17, 2026; Meta announced an updated Superintelligence Scaling Framework on October 2, 2026. OpenAI’s public overview describes its Preparedness Framework but does not establish a current version number, and the company has indicated that its main safety document was being rewritten. Those dates and qualifications are part of any honest comparison.
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What a cross-lab comparison should measure
A safety framework is a set of stated processes and controls. It is not, by itself, proof that controls were implemented, that a threshold is calibrated correctly, or that one lab’s evaluation result can be compared numerically with another’s. Start by recording the framework’s exact name, version, effective or publication date, and the documents that contain model-specific results.
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- Covered threat models.
- Security for model weights.
- Deployment mitigations.
- Conditions for halting development or deployment.
- Evaluation designed to elicit a model’s full relevant capability.
- Evaluation timing and frequency.
- Accountability.
- Policy updates.
That taxonomy is a useful starting point, but it predates the 2026 revisions and is not an implementation audit. Use it to organize questions, not to assign a safety score.
The six comparison axes
| Axis | Questions to ask | How to interpret the answer |
|---|---|---|
| Threat scope | Are cyber, biological or chemical, persuasion, autonomy, AI research and development, loss of control, or other risks explicitly named? | Separate named categories from broad language such as “advanced AI risks.” A missing category may mean it is out of scope, covered elsewhere, or not publicly specified. |
| Evaluations and thresholds | What capability is tested, when are tests run, what constitutes crossing a threshold, and what evidence supports that threshold? | Compare each lab’s test design and reported results separately. Identically named levels do not necessarily measure the same capability. |
| Mitigations | What changes after a threshold is reached: stronger weight security, restricted access, deployment safeguards, monitoring, containment, or a pause? | Record the trigger and the required control together. A high threshold with no specified response is not equivalent to a lower threshold with mandatory safeguards. |
| Stop/go authority | Who reviews or approves training and deployment? Can anyone halt an activity when safeguards are inadequate? | Make the lifecycle boundary explicit. A deployment board may not control the earlier decision to train a model of a particular scale or capability. |
| Accountability and transparency | Are there board reviews, outside reviewers, risk reports, model cards, incident processes, or public evaluation data? | Count documents that are actually available and note redactions. A promise to publish a report is different from a published report with methods and results. |
| Revision and freshness | What version is in force, when did it take effect, and what changed? | Policy language can change faster than a comparison article. Check the official change history and date every claim. |
Current frameworks at a glance
| Lab | Current public document and date | Threats and lifecycle emphasis | Evaluation and mitigation mechanisms described publicly | Decision authority and evidence |
|---|---|---|---|---|
| Anthropic | Responsible Scaling Policy v3.4; effective July 8, 2026. Policy page last updated August 14, 2026. | Catastrophic risks addressed by the RSP, including automated AI research and development and novel chemical or biological weapons production thresholds described in the v3.4 summary. | Capability thresholds trigger changes to safeguards and access. The v3.4 summary also revises internal access requirements for unredacted risk reports. | Risk reports describe the company’s view of risk and preparedness. The August 2026 report uses a July 15 coverage date and explains redactions and external-review arrangements. |
| OpenAI | Public “Approach to Frontier Risk” overview; accessed October 13, 2026. No current version number is established by the overview. | Cybersecurity, persuasion, chemical and biological threats, and autonomy are among the tracked categories. The framework is intended to cover capability gains from scale, algorithmic improvements, or other optimizations. | Rigorous capability evaluations and monitoring are paired with governance and accountability. The overview distinguishes deployment decisions above a capability threshold from earlier decisions about whether to train models at a given scale or capability. | A joint Deployment Safety Board with Microsoft addresses certain deployment decisions. The overview is evolving; do not treat older threshold figures as current without the latest official Preparedness Framework text. |
| Google DeepMind | Frontier Safety Framework v3.1, dated April 17, 2026. | Frontier-model risks covered by the FSF, alongside broader responsibility and safety practices. | Frontier models undergo evaluations. The framework and model-specific safety reports describe thresholds and mitigation plans; summaries appear in model cards. | Safety reports are the key public evidence source. The overview does not establish that every model has identical testing or fully disclosed results. |
| Meta | Updated Superintelligence Scaling Framework announced October 2, 2026. | Requirements apply before training runs and deployment. The update expressly addresses loss of control during training and evaluation, as well as open-weight release considerations. | Sandbox review, mitigation or exclusion of high-severity vulnerabilities, real-time secure logging of outputs and trajectories, and automated monitoring of high-risk rollouts that can stop a run. | These are measures Meta describes in its announcement. The post does not independently validate their effectiveness or provide a common outcome measure with the other labs. |
Anthropic: Responsible Scaling Policy v3.4
What changed and when
Anthropic’s policy page lists RSP v3.4 as effective July 8, 2026 and says the page was last updated August 14, 2026. The v3.4 summary revises the automated AI research-and-development threshold and the threshold for novel chemical or biological weapons production. It also changes internal access requirements for unredacted risk reports, permits a report to state a coverage date rather than relying only on its publication date, requires public reports to identify where material was redacted, and clarifies how external reviewers may divide review of unredacted sections.
These are governance and reporting changes as well as technical changes. A comparison should therefore record both the capability trigger and the information-control process around it.
How to read Anthropic’s risk reports
Anthropic says, “Our Risk Reports aim to provide a direct, candid, and informative description of how we see the risks of our systems and our state of preparedness for them (particularly the catastrophic risks addressed in our Responsible Scaling Policy).” That is the company’s description of its reporting purpose, not an independent finding about preparedness.
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The August 2026 report covers risks and actions through a July 15 coverage date. Do not treat July 15 as the report’s publication date, and do not assume that a later publication automatically includes events after the stated coverage date. When comparing reports, record the coverage period, publication date, redactions, and whether an external reviewer saw the unredacted material.
OpenAI: Preparedness Framework and deployment governance
What the public overview establishes
OpenAI’s frontier-risk overview describes a Preparedness Framework built around capability evaluations and monitoring, governance and accountability, and tracked categories that include cybersecurity, persuasion, chemical and biological threats, and autonomy. It says the framework complements existing safety work.
OpenAI states: “The Preparedness Framework governs our development of increasingly capable frontier models regardless of whether those increasing capabilities stem from scale, algorithmic improvements, or other optimizations.” This wording matters because a model can become more capable through an algorithmic change without a proportionate increase in training compute.
Deployment authority is not training authority
The overview describes a joint Deployment Safety Board with Microsoft for deployment decisions above a capability threshold. It separately explains that the decision about whether to train models at a particular scale or capability occurs earlier. A table that simply labels OpenAI as having a “stop board” would therefore overstate the board’s scope. Record which body can act, at which lifecycle stage, and whether its decision is binding.
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Why OpenAI requires a version-date caveat
The overview does not provide a current version number, and contemporaneous reporting in August 2026 said OpenAI was rewriting its main safety document. Older thresholds or levels from a 2024 comparison should not be presented as the current OpenAI policy. For a publication-date comparison, consult the latest official Preparedness Framework document and identify its effective date before assigning any threshold or requirement.
Google DeepMind: Frontier Safety Framework v3.1
Framework and evidence trail
Google DeepMind’s Frontier Safety Framework page identifies version 3.1 and dates it April 17, 2026. The page says frontier models undergo evaluations and directs readers to safety reports for full evaluation results and mitigation plans, with summaries in model cards.
This creates a two-level evidence trail: the framework states the intended process, while an individual safety report should contain the model-specific testing and mitigations. A model card may summarize that evidence, but it should not be assumed to reproduce every method, limitation, or result in the full report.
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Use the versioned framework to identify what a threshold is meant to trigger, then use the relevant safety report to determine whether the test was run, what capability was observed, and which mitigations were selected. Do not infer that all models received identical evaluations or that an overview statement proves a particular model cleared a particular level.
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Meta: Superintelligence Scaling Framework update
Controls before training and deployment
In its October 2, 2026 announcement, Meta says: “Our Framework outlines how we meet that responsibility by outlining the capabilities we test for, the thresholds a model must clear, and the requirements we place on our safety and security systems, before a training run begins and before a model is deployed.” This places pre-training decisions and deployment decisions in the same comparison, rather than treating safety as a final release gate only.
Containment during training and evaluation
The update expands loss-of-control requirements to containment during training and evaluation. Meta describes sandbox review, mitigation or exclusion of high-severity vulnerabilities, real-time secure logging of outputs and trajectories, and automated monitoring of high-risk rollouts that can stop a run.
These mechanisms answer a different question from a deployment-only policy: what happens while a model is still being trained or tested and could potentially discover ways around controls? Compare the trigger, the monitoring coverage, the authority to stop a run, and the evidence that a stop actually occurred. Meta’s announcement describes the controls but does not independently establish how effective they are.
Open-weight release as a separate case
Meta’s update also discusses open-weight releases. An open-weight model can be copied and operated outside the original lab’s infrastructure, so deployment controls that depend on a hosted service may not transfer completely. Include open-weight assumptions in the comparison: what risks are evaluated before release, which safeguards remain available after release, and what the framework says about downstream use.
Why the four frameworks are not directly scoreable
Different definitions and test designs
“Autonomy,” “cyber capability,” “loss of control,” and “AI research and development” can refer to different test environments, task sets, time horizons, and success criteria. A threshold name is not a common unit. Before comparing two thresholds, document the task, evaluator, model access, prompting conditions, and pass criterion.
Different lifecycle boundaries
One framework may impose requirements before a training run, another may focus on deployment, and a third may make its strongest commitment in a risk report after evaluation. These are not contradictory measurements; they are controls at different points in the lifecycle. A comparison should show the boundary instead of collapsing every control into a single maturity label.
Policy text versus implementation evidence
The official lab pages establish stated frameworks, dates, and selected process descriptions. They do not, on their own, prove that controls worked in production, that a lab followed every stated procedure, or that an external reviewer agreed with the lab’s conclusion. Keep four columns in your notes: policy commitment; evidence the lab reports; independent assessment; observed outcome.
Publication date versus coverage date
A report published in August can describe events only through a July coverage date. A framework effective in July can be revised by an August page update. Put both dates beside the claim so readers can tell what period the evidence actually covers.
A repeatable method for comparing any two labs
- Freeze the comparison date. State the date on which you checked each official framework. For this comparison, Anthropic’s page was last updated August 14, 2026, Google DeepMind’s FSF is dated April 17, 2026, Meta’s update was announced October 2, 2026, and OpenAI’s overview was accessed October 13, 2026.
- Capture the exact document identity. Record the title, version, effective date, publication date, and any stated coverage date. Mark OpenAI’s version as not stated in the overview rather than guessing one.
- Build a threat map. List every explicit category, then a separate list of broad or unspecified risks. Do not treat a category mentioned in a press announcement as equivalent to a fully defined test in a framework.
- Describe the evaluation. Write down what is tested, who runs it, when it runs, how capability is elicited, and what evidence supports the threshold. Keep test results in a separate column from intended procedures.
- Pair every trigger with its response. Note whether crossing a threshold changes weight security, access, deployment, containment, monitoring, staffing, or release status. If the response is not stated, write “not stated” and name the document.
- Map authority by lifecycle stage. Identify who can approve, delay, or stop training, evaluation, and deployment. OpenAI’s overview is a clear example of why deployment authority and training authority must be recorded separately.
- Audit transparency. List public risk reports, model cards, evaluation results, redaction notices, incident disclosures, and external reviews. Distinguish an available report from a commitment to publish one.
- Check for release-model assumptions. Hosted deployment, API access, restricted research access, and open-weight release create different control surfaces. Record which assumptions the policy makes.
- Write the uncertainty next to the claim. Say when a result is self-reported, when a document is only an overview, and when no common outcome statistic exists. Do not turn missing evidence into a ranking.
What to monitor after publication
- New framework versions and effective dates, especially changes to threshold definitions or required mitigations.
- Model-specific evaluation reports that show methods, results, limitations, and the date through which evidence is current.
- External review arrangements, including which sections reviewers can access and how redactions are disclosed.
- Incident reports or documented cases in which monitoring, containment, or a stop authority was used.
- Changes to open-weight release policies and to controls that operate during training and evaluation rather than only at deployment.
- Evidence that safeguards work in practice, not merely that a policy continues to promise them.
What the 2024 METR statistic does—and does not—tell you
METR reported in November 2024 that sixteen companies agreed to the Frontier AI Safety Commitments at the AI Seoul Summit. That is a dated figure published by METR in 2024, not a 2026 count of laboratories with current, detailed safety frameworks. It shows that policy commitments had broad participation at that summit; it does not establish comparable implementation, effectiveness, or current participation by each company.
How to state a defensible conclusion
The evidence supports a process comparison, not a winner ranking. Anthropic provides a dated, versioned scaling policy and risk-report process; OpenAI describes evaluations, tracked frontier-risk categories, and a deployment board while leaving the current version and training-stage authority less explicit in its overview; Google DeepMind pairs a versioned framework with model safety reports and model-card summaries; Meta’s October update emphasizes requirements before training and deployment, containment during training and evaluation, and open-weight considerations.
Those differences are meaningful, but none of the cited policy pages supplies a common, independently verified outcome measure across all four labs. The strongest comparison is therefore the one that shows what each lab says it will do, what evidence it has actually published, who can intervene, and how the rules have changed—without claiming that policy language alone proves safety.
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