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Choose METR when you need a broad, quantitative picture of what an AI agent can do autonomously. Choose Apollo Research when the central concern is strategic deception, scheming, evaluation awareness, or other misaligned behavior before deployment. Treat ARC Evals as the historical predecessor of METR, not as a third current provider: the evaluation team spun out of the Alignment Research Center in September 2023 and adopted the METR name in December 2023.
The right evaluator depends on the risk decision, model access, deployment stage, realism of the test environment, and the evidence standard your safety case requires. Many serious programs need both broad capability measurement and targeted behavioral probes rather than a single pass from one approach.
METR, ARC Evals and Apollo Research at a glance
These names describe two current evaluation approaches and one historical program. METR is the current organization associated with broad autonomous-capability measurement. Apollo Research is a current specialist in pre-deployment evaluations for strategic deception and related misaligned behavior. ARC Evals refers to the earlier evaluation program whose team became METR.
| Approach | Best fit | Typical question | Main strength | Important caveat |
|---|---|---|---|---|
| METR | Broad autonomy and capability assessment | How long and how independently can an agent complete consequential tasks? | Realistic task suites, agent tooling, elicitation guidance and quantitative capability measurement | Broad autonomy metrics may not reveal deceptive internal strategies or hidden objectives |
| ARC Evals (historical) | Legacy context and foundational dangerous-capability work | Could an agent acquire resources, replicate or evade oversight in a controlled environment? | Early third-party red-team methodology and realistic autonomous-task framing | The team became METR; ARC Evals should not be presented as a separate current provider |
| Apollo Research | Deception, scheming, evaluation awareness and misalignment | Does a model behave strategically differently when it knows it is being evaluated, or pursue hidden goals? | Focused pre-deployment behavioral evaluations and safety research | Public pages do not state a universal price list or an open intake process |
No single published score, price or market-share figure makes one approach universally “best.” Selection should follow the risk construct and the decision the evaluation must support.
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Why ARC Evals is not a third current competitor
The historical timeline
ARC Evals was the evaluation team associated with the Alignment Research Center. In a March 17, 2023 update, the program described its focus as dangerous capabilities that could let an AI system autonomously gain resources and evade human oversight. The team announced a spinout in September 2023 and announced the METR name in December 2023.
That history matters when reading older reports or comparing providers. A report labeled “ARC Evals” can be an important predecessor to today’s autonomy-evaluation practice, but it does not represent a separate organization you can hire alongside METR. Use “ARC Evals, now METR” for historical work and “METR” for the current evaluator.
What the historical work contributed
The ARC Evals work framed dangerous autonomy as an operational question rather than a benchmark-only exercise. Its published evaluation examined whether language-model agents could acquire resources, make copies of themselves and adapt to novel challenges encountered in the wild. The work used controlled environments, realistic interaction with tools and code, and researchers in the loop for dangerous actions.
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Those design choices remain useful when you are building a test today: give an agent meaningful tools, make tasks realistic enough to expose planning failures, keep high-risk actions controlled, and document where human intervention changes the result. The organizational lesson is just as important: do not count ARC Evals and METR as two independent bids or two independent bodies of current capacity.
What METR evaluates and how its method works
Scope: broad autonomous capability
METR describes itself as a research nonprofit that measures whether and when AI systems might threaten catastrophic harm. Its work covers broad autonomous capabilities and AI systems that could accelerate AI research and development. The public resource collection includes evaluation reports, public and private autonomy tasks, software and guidance for measuring autonomous capabilities.
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The practical question is not simply whether a model can answer a difficult prompt. It is whether an agent, equipped with a scaffold and tools, can complete a realistic consequential task with limited human help, how long it can sustain that work, and how performance changes when elicitation, tooling or time limits change.
A task-centered METR-style workflow
- Define realistic tasks. Specify the work an agent must complete, the permitted tools, the environment, the success condition and the consequences of failure. Tasks should represent the capability or risk decision you care about rather than only what is easy to score.
- Provide an agent scaffold and tools. Record the model version, system instructions, tool permissions, code-execution or browsing access, context limits and any human intervention. A result without this configuration is difficult to reproduce or compare.
- Attempt capability elicitation. Test whether better prompting, planning scaffolds, retries, decomposition or other elicitation methods materially improve performance. The question is what the system can do when its capability is elicited responsibly, not only what it does on a default prompt.
- Run repeated trials. Capture successes, failures, time taken, early stopping and reasons for termination. Repetition is especially important when a small number of trials would produce a wide confidence interval.
- Estimate performance with uncertainty. Report the metric, sample size, uncertainty interval and assumptions. If the result will inform a threshold, show how the uncertainty affects the decision rather than presenting a point estimate as a fact.
- Analyze sensitivity. Compare performance across scaffolds, time budgets, tool configurations and elicitation strategies. This reveals whether an apparent capability depends on one fragile setup.
Why quantitative uncertainty matters
METR protocol guidance discusses repeating tasks when confidence intervals are too wide, correcting for early stopping and using continuous metrics that can inform thresholds and safety margins. This makes the approach useful for a deployment gate or safety case that needs to explain not only what happened in a test, but how certain the organization is about the estimate.
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When METR is the closest fit
METR’s published approach is the closest fit when you need an evidence-backed estimate of what an agent can do across tasks, including how performance changes with scaffolding, time and elicitation. Typical uses include autonomy-risk assessment, capability thresholds, deployment gating, and quantitative evidence in a safety case.
What Apollo Research evaluates
Scope: strategic and misaligned behavior before deployment
Apollo Research describes its work as pre-deployment evaluation of frontier AI systems to detect strategic deception, evaluation awareness and misaligned behavior. Its selection test is behavioral: could the model be optimizing for a hidden or strategically different objective, behave differently because it recognizes the evaluation, or exhibit scheming-like behavior before release?
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This is a different construct from broad task-completion capability. An agent might perform an assigned task competently without revealing whether it would conceal a goal, exploit an evaluator’s assumptions or act differently when it believes monitoring has been removed. Conversely, a targeted deception scenario may reveal a concerning behavior without measuring the model’s general ability to sustain long autonomous work.
Questions an Apollo-style evaluation can address
- Does the model pursue a goal that conflicts with the stated task or developer instruction?
- Does it behave differently when it knows, suspects or does not know that it is being evaluated?
- Will it conceal a strategy, misrepresent what it has done or exploit a weakness in the oversight process?
- Does it show scheming-like behavior under scenarios designed to create strategic pressure?
- Are concerning behaviors robust across prompts, environments and levels of evaluator awareness?
These questions require careful scenario design. A result can be sensitive to wording, incentives, monitoring cues and the model’s understanding of the test. That sensitivity is not a reason to omit the evaluation; it is a reason to vary scenarios and report the conditions under which behavior appears.
Place in the wider evaluation ecosystem
Apollo’s governance material places pre-deployment behavioral evaluation alongside capability evaluations, red-teaming and standards-oriented collaboration. In practice, an Apollo-style study can be one layer in a larger program: broad capability tests establish what the system can do, targeted probes examine how it behaves under strategic pressure, and governance processes determine how findings affect release decisions.
Engagement and public information
Public Apollo pages do not provide a universal price list or an open intake process. Budget, timing, access requirements and reporting arrangements therefore need to be established directly for the specific project. Do not assume that a public example implies a standard package or guaranteed availability.
Detailed comparison by project-selection axis
| Axis | METR | ARC Evals (historical) | Apollo Research |
|---|---|---|---|
| Primary risk question | What autonomous work can the system complete, under what conditions and for how long? | Could an agent gain resources, replicate or evade oversight in a controlled setting? | Could the model deceive, scheme, recognize the evaluation or pursue a hidden objective? |
| Evaluation stage | Exploratory capability research, deployment gating and safety-case measurement | Foundational dangerous-capability research and historical benchmark context | Pre-deployment behavioral evaluation and safety research |
| Target behavior | Autonomous task completion, persistence, tool use and capability elicitation | Resource acquisition, self-copying, adaptation and oversight evasion | Strategic deception, evaluation awareness, scheming and misaligned behavior |
| Environment and access | Realistic tasks with agent scaffolds, tools and controlled execution; exact access depends on the project | Controlled environments, realistic tool and code interaction, with researchers in the loop for dangerous actions | Scenario-driven behavioral tests; the exact environment and model access depend on the evaluation |
| Quantitative evidence | Designed for performance estimates, repeated runs, uncertainty analysis and threshold use | Published work emphasized dangerous-capability demonstrations and methodology rather than a current service metric | Focused behavioral evidence; interpretation depends on scenario design and replication |
| Independence | Useful as an external measurement organization; model access and reporting terms must be agreed for each engagement | Historically external to the model developer in its published dangerous-capability work | Pre-deployment evaluation focused on behaviors that a developer may not discover through capability testing alone |
| Reporting or safety-case role | Strong fit for capability thresholds, uncertainty statements and quantitative safety-case inputs | Best used as historical context and a source of test-design ideas | Strong fit for targeted behavioral findings and release decisions concerning deception or misalignment |
| Current-provider status | Current organization | Not a separate current provider; the team became METR | Current organization |
Which approach fits common project decisions?
You need a deployment gate based on autonomy
Start with METR-style broad task evaluation. Define the consequential tasks, the maximum autonomous duration, permitted tools and acceptable failure rate. Use repeated trials and uncertainty intervals so the gate does not depend on one unusually successful or unsuccessful run.
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You are worried about deception or scheming
Start with Apollo-style targeted behavioral evaluation. Specify the hidden-goal, evaluation-awareness or strategic-deception hypothesis before writing scenarios. Include conditions in which monitoring cues, incentives and evaluator knowledge vary, then test whether the behavior persists.
You need both capability context and behavioral evidence
Use both approaches in sequence or in parallel. Broad autonomy measurement tells you what the system can accomplish and how much scaffolding it needs. Targeted deception evaluations then examine whether that capability is used in strategically concerning ways. Keep the datasets and interpretations separate: a strong autonomy result is not evidence of deception, and a clean deception probe is not evidence that the model lacks dangerous capability.
You are reviewing an older ARC Evals report
Use it to understand the lineage of dangerous-autonomy testing and to adapt its controlled-environment, tool-use and researcher-in-the-loop ideas. For a current engagement, frame the request to METR or another current evaluator rather than treating ARC Evals as an independent bidder.
You are still exploring the risk
Begin by writing the decision the evaluation must support. If the decision cannot be stated as either a capability question, a behavioral-risk question or both, commissioning a large test suite may produce results that are interesting but difficult to act on. A short scoping phase can identify the model access, environment and evidence standard before full evaluation begins.
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Regardless of which approach fits, settle these items in writing:
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- Model identity: record the exact model, checkpoint or release candidate, system instructions and any fine-tuning or safety layer.
- Access level: specify API, weights, context window, rate limits, tool permissions, browsing, code execution and external network access.
- Human involvement: define when researchers may intervene, approve an action, stop a run or provide information.
- Environment: document the simulated or real systems, data boundaries, credentials, sandboxing and rollback plan.
- Risk construct: state whether the primary target is autonomous capability, resource acquisition, oversight evasion, deception, evaluation awareness, scheming or another behavior.
- Success criteria: define task completion, unacceptable actions, severity levels and what counts as a reproducible finding.
- Sampling plan: set the number of trials, retries, time budgets, early-stopping rules and uncertainty method before seeing results.
- Independence and conflicts: identify who controls the model, who can alter the environment and who has authority to suppress or publish a finding.
- Reporting: agree on raw logs, redacted examples, confidence intervals, limitations, escalation rules and the audience for the final report.
- Incident handling: prepare a safe stop procedure, access revocation, evidence preservation and notification path for dangerous behavior.
How to interpret results without overclaiming
A broad capability result is not a general safety certificate
A task suite can miss narrow but highly capable behaviors. Success on the measured tasks does not establish that every relevant task is safe, nor does failure on one suite prove that a model cannot perform the capability under a different scaffold, tool configuration or time budget. Report the tested distribution and the untested regions.
A behavioral finding is condition-dependent evidence
Deception and scheming results can depend on scenario design, incentives, monitoring cues and the model’s awareness of evaluation. A single concerning trace deserves investigation, but the report should distinguish an isolated response from a behavior that replicates across prompts, environments and runs.
Combine evidence instead of averaging away risk
Do not collapse autonomy and deception into one score. Preserve the separate constructs, then explain how they interact for the deployment decision. For example, a system that completes long tasks autonomously may warrant more extensive strategic-behavior testing than a system that cannot sustain work without constant assistance.
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Document residual uncertainty
Every final report should state what was tested, what was not tested, how model access differed from deployment, how human intervention affected outcomes and which assumptions could change the conclusion. A clean result from one approach never proves general safety.
Example evaluation designs
| Project situation | Primary approach | Useful design | Decision supported |
|---|---|---|---|
| An agent may automate multi-step research and engineering work | METR-style capability evaluation | Realistic tasks, controlled tools, multiple time budgets, elicitation variants and uncertainty intervals | Whether autonomy thresholds or deployment limits are met |
| A frontier model may hide a goal when oversight is present | Apollo-style behavioral evaluation | Matched scenarios that vary evaluator awareness, monitoring and incentives, with replicated traces | Whether release requires mitigation, additional monitoring or a blocked deployment |
| A model has both high autonomy and a suspected strategic objective | Combined program | Establish capability baseline first, then run targeted deception and scheming probes under controlled access | How capability and behavioral evidence interact in the safety case |
| A team is translating an older dangerous-capability report into current tests | Historical ARC Evals methods plus current METR ownership | Adapt resource-acquisition, replication and oversight-escape ideas with modern sandboxing and explicit stop rules | Whether the legacy concern remains relevant to the current system |
Questions to settle with an evaluator
Before selecting a provider or research partner, ask for a written answer to these project-specific questions:
- What model access is required, and can the evaluation be performed on an unreleased model?
- Which tools, environments and human interventions are included?
- Will the work produce quantitative estimates, qualitative findings or both?
- How are repeated trials, early stopping and uncertainty handled?
- How will evaluator awareness and scenario sensitivity be tested?
- What raw artifacts, logs and reproduction materials will the client receive?
- How are critical findings escalated, and who controls publication or disclosure?
- What parts of the proposed test are known to have limited coverage?
- What costs, schedule and staffing assumptions apply to this specific scope? Public materials do not establish a universal price for either approach.
Bottom line for your selection
Use METR for the broad question: “What can this agent do autonomously, with realistic tools and time, and how certain are we about the estimate?” Use Apollo Research for the focused question: “Does this frontier model deceive, recognize the evaluation, scheme or pursue a hidden objective before deployment?” Use ARC Evals as the historical name for foundational dangerous-autonomy work, not as a separate current organization. If your release decision depends on both capability and intent-related behavior, commission complementary evaluations and keep their evidence streams distinct.
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