Select a model to see a summary that provides quick access to essential information about Claude models, condensing key details about the models' capabilities, safety evaluations, and deployment safeguards. We've distilled comprehensive technical assessments into accessible highlights to provide clear understanding of how the models function, what they can do, and how we're addressing potential risks.
Claude Opus 4.5 Summary Table
Model description
Claude Opus 4.5 is our new hybrid reasoning large language model. It is state-of-the art among frontier models on software coding tasks and agentic tasks that require it to run autonomously on a user’s behalf.
Benchmarked Capabilities
See our Claude Opus 4.5 system card’s Section 2 on capabilities.
Claude Opus 4.5 can understand both text (including voice dictation) and image inputs, engaging in conversation, analysis, coding, and creative tasks. Claude can output text, including text-based artifacts, diagrams, and audio via text-to-speech.
Knowledge Cutoff Date
Claude Opus 4.5 has a knowledge cutoff date of May 2025. This means the model’s knowledge base is most extensive and reliable on information and events up to May 2025.
Software and Hardware Used in Development
Cloud computing resources from Amazon Web Services and Google Cloud Platform, supported by development frameworks including PyTorch, JAX, and Triton.
Model architecture and training methodology
Claude Opus 4.5 was pretrained on a proprietary mix of large, diverse datasets to acquire language capabilities. After the pretraining process, Claude Opus 4.5 underwent substantial post-training and fine-tuning, with the intention of making it a helpful, honest, and harmless assistant. This involved a variety of techniques including reinforcement learning from human feedback and reinforcement learning from AI feedback.
Training Data
Claude Opus 4.5 was trained on a proprietary mix of publicly available information from the internet up to May 2025, non-public data from third parties, data provided by data-labeling services and paid contractors, data from Claude users who have opted in to have their data used for training, and data generated internally at Anthropic.
Testing Methods and Results
Based on our assessments, we have decided to deploy Claude Opus 4.5 under the ASL-3 Standard. See below for select safety evaluation summaries.
The following are summaries of key safety evaluations from our Claude Opus 4.5 system card. Additional evaluations were conducted as part of our safety process; for our complete publicly reported evaluation results, please refer to the full system card.
Safeguards
Claude Opus 4.5 showed improvements across the board on our standard evaluations for harmlessness compared with previous models. When tested with prompts where user intent was unclear (for example, requests that could have either benign explanations or potentially concerning motivations), Opus 4.5 demonstrated greater skepticism, more often asking clarifying questions before providing sensitive information or being transparent about its reasoning when declining to answer. On multi-turn evaluations assessing Claude's willingness to provide harmful information in longer conversations, Claude Opus 4.5 performed similar or better in all 10 risk areas tested compared to Claude Opus 4.5.
For this release, we also expanded our single-turn evaluations to cover languages beyond English, namely Arabic, French, Korean, Mandarin Chinese, and Russian. We selected these languages to balance global popularity with linguistic diversity, covering a range of character systems, text directions, and syntactic structures. Claude Opus 4.5 achieved a 99.78% harmless response rate on single-turn violative requests across all tested languages, outperforming all previous models.
To complement our existing evaluations for agentic safety, we updated and added new evaluations measuring how the model responds to harmful tasks in computer and browser use environments, respectively. The first evaluation formally covers the agentic computer use risks outlined in an addendum to our Usage Policy, focusing on surveillance and unauthorized data collection, generation and distribution of harmful content, and scaled abuse. Claude Opus 4.5 was our strongest model yet on this evaluation, refusing to comply with 88.39% of requests, compared to 66.96% for Claude Opus 4.1. The second was an adaptive evaluation to measure the robustness of our Claude for Chrome extension against prompt injection. For each environment, an adaptive attacker was given 100 attempts to craft a successful injection. With new safeguards in place, only 1.4% of attacks were successful against Claude Opus 4.5, compared to 10.8% for Claude Sonnet 4.5 with our previous safeguards.
Evaluation Awareness
When testing scenarios designed to probe model behavior, models can sometimes recognize that they are being evaluated. Claude Sonnet 4.5 and Haiku 4.5 showed this behavior much more often than prior models, though we were nonetheless able to conduct what we believe was a sufficiently thorough assessment of its behavior using a combination of changes to our evaluations and assessment methods based on white-box interpretability.
With Claude Opus 4.5, we removed some components of our training process that we suspected were exacerbating evaluation awareness, while being careful to not actively discourage the model from verbalizing this evaluation awareness when it occurs. We believe these mitigations were partially, though not entirely, effective at reducing the influence of evaluation awareness on the model's alignment-relevant behavior. Between these interventions and evaluation-strengthening measures like those we used with Sonnet 4.5 and Haiku 4.5, we were able to complete an assessment of Opus 4.5’s behavior, and find it to be our strongest model yet on most safety and alignment dimensions. While we cannot fully determine the origin of verbalized evaluation awareness in Claude Opus 4.5, we hypothesize that it can be at least partially attributed to training received by the model, to help it reason more thoughtfully about the motivation behind user prompts.
RSP Evaluations
Our Responsible Scaling Policy (RSP) evaluation process is designed to systematically assess our models' capabilities in domains of potential catastrophic risk before releasing them. Based on these assessments, we have decided to release Claude Opus 4.5 under the ASL-3 Standard.
Claude Opus 4.5 showed strong performance across many evaluations, warranting a comprehensive assessment to determine whether it had reached the ASL-4 threshold. We determined that Claude Opus 4.5 does not cross this threshold. However, the model is approaching or surpassing high levels of capability in our "rule-out" evaluations — early proxies designed to indicate whether a model might be nearing the next capability threshold. We are launching Claude Opus 4.5 with ASL-3 safeguards, which we believe are appropriate.
CBRN Evaluations
CBRN (which stands for Chemical, Biological, Radiological, and Nuclear) evaluations are designed to measure the ability of our model to significantly help individuals or groups with basic technical backgrounds (e.g., undergraduate STEM degrees) to create, obtain, and deploy CBRN weapons (ASL-3 capability threshold) or substantially uplift moderately-resourced state programs (ASL-4 capability threshold). We primarily focus on biological risks with the largest consequences, such as enabling pandemics.
We conducted multiple types of biological risk evaluations, including human uplift studies, evaluations from biodefense experts, multiple-choice and open-ended knowledge and skills evaluations, and task-based agentic evaluations. Overall, Claude Opus 4.5 performed as well as or slightly better than Claude Opus 4.1 and Claude Sonnet 4.5 across a suite of tasks designed to test factual knowledge, reasoning, applied skillsets, and creativity in biology. Most notably, however, in an expert uplift trial, Claude Opus 4.5 was meaningfully more helpful to participants than previous models, leading to substantially higher scores and fewer critical errors, but still produced critical errors that yielded non-viable protocols. We take this as an indicator of general model progress where a clear rule-out of the next capability threshold may soon be difficult or impossible under the current regime. For this reason, we are specifically prioritizing further investment into threat models, evaluations, tests, and safeguards that will help us make more precise judgments about the ASL-4 threshold for CBRN risk.
Autonomous AI R&D Evaluations
Models capable of autonomously conducting significant amounts of AI R&D could pose numerous risks. One category of risk would be greatly accelerating the rate of AI progress, to the point where our current approaches to risk assessment and mitigation might become infeasible. Additionally, we see AI R&D as a potential early warning sign for broader R&D capabilities and high model autonomy, in which case both misaligned AI and threats from humans with access to disproportionate compute could become significant.
To cross the AI R&D-4 capability threshold, the model must have “the ability to fully automate the work of an entry-level, remote-only Researcher at Anthropic.” This is a very high threshold of robust, long-horizon competence, and is not merely a stand-in for “a model that can do most of the short-horizon tasks that an entry-level researcher can do.” We judge that Claude Opus 4.5 could not fully automate an entry-level, remote-only research role at Anthropic.
We tested Claude Opus 4.5 on various evaluation sets designed to measure AI research and software engineering capabilities, including tasks like writing GPU kernels, training language models, implementing reinforcement learning algorithms, and resolving real-world software engineering issues. On several of these benchmarks, the model has reached our pre-defined rule-out thresholds. These benchmarks serve as early proxies for the actual capability threshold — the ability to fully automate the work of an entry-level, remote-only AI researcher — which requires robust, long-horizon competence beyond just performing well on short-horizon tasks.
We also surveyed 18 Anthropic researchers, who were themselves some of the most prolific users of the model in Claude Code, and none believed the model could completely automate the work of a junior ML researcher or engineer. We believe Claude Opus 4.5 would struggle to problem-solve, investigate, communicate, and collaborate over multiple weeks in the way a junior researcher could, and would lack the situational judgment that characterizes long-term human work. Based on these findings, we determined that Claude Opus 4.5 does not pose the autonomy risks specified in our AI R&D-4 threat model.
Cybersecurity Evaluations
The RSP does not stipulate formal thresholds for cyber capabilities. Instead, we conduct ongoing assessments against two threat models: scaling attacks by unsophisticated non-state actors, and enabling low-resource states to conduct catastrophe-level attacks.
We tested Claude Opus 4.5 on a range of cybersecurity challenges developed with expert partners, designed to be more difficult and more representative of real-world tasks than publicly available benchmarks. These included challenges across web security, cryptography, binary exploitation, reverse engineering, and network operations. Claude Opus 4.5 showed improved performance across these categories, including the first successful solve of a network challenge by a Claude model without human assistance. These improvements are consistent with the model's general advances in coding and long-horizon reasoning, rather than a sudden leap in offensive cyber capabilities. Critically, we do not see evidence that the model can autonomously execute the kind of sophisticated, multi-step attack chains described in our second threat model. Based on these results, we believe Claude Opus 4.5 does not demonstrate catastrophically risky capabilities in the cyber domain.
However, models are increasingly being used in real-world cyberattacks. Recently, we discovered and disrupted a case of cybercriminals using “vibe hacking” to carry out extortion attempts with the help of models; we also discovered and disrupted GTG-1002, which we assess was a state-sponsored cyberespionage campaign in part automated by AI. As models become more autonomous and capable in cybersecurity, the threat dynamic may change, requiring us to reconsider the appropriate assessments we perform and mitigations we enact in keeping with the RSP.
Claude Haiku 4.5 Summary Table
Model description
Claude Haiku 4.5 is our new hybrid reasoning large language model in our small, fast model class.
Claude Haiku 4.5 can understand both text (including voice dictation) and image inputs, engaging in conversation, analysis, coding, and creative tasks. Claude can output text, including text-based artifacts, diagrams, and audio via text-to-speech.
Knowledge Cutoff Date
Claude Haiku 4.5 has a knowledge cutoff date of February 2025. This means the models’ knowledge base is most extensive and reliable on information and events up to February 2025.
Software and Hardware Used in Development
Cloud computing resources from Amazon Web Services and Google Cloud Platform, supported by development frameworks including PyTorch, JAX, and Triton.
Model architecture and Training Methodology
Claude Haiku 4.5 was pretrained on a proprietary mix of large, diverse datasets to acquire language capabilities. After the pretraining process, the model underwent substantial post-training and fine-tuning, the object of which is to make it a helpful, honest, and harmless assistant. This involves a variety of techniques, including reinforcement learning from human feedback and from AI feedback.
Training Data
Claude Haiku 4.5 was trained on a proprietary mix of publicly available information on the Internet as of February 2025, non-public data from third parties, data provided by data-labeling services and paid contractors, data from Claude users who have opted in to have their data used for training, and data we generated internally at Anthropic.
Testing Methods and Results
Based on our assessments of the model’s demonstrated capabilities, we have deployed Claude Haiku 4.5 under the ASL-2 Standard as described in our Responsible Scaling Policy. See below for select safety evaluation summaries.
The following are summaries of key safety evaluations from our Claude Haiku 4.5 system card. Additional evaluations were conducted as part of our safety process; for our complete publicly reported evaluation results, please refer to the full system card.
Agentic Safety and Malicious Use
As AI systems become more capable at autonomously completing complex tasks, keeping these workflows safe is critical. We evaluated Claude Haiku 4.5's ability to refuse requests to write harmful code – situations where users try to get the model to create malware, hacking tools, or other malicious software that violates our Usage Policy.
We tested Claude Haiku 4.5 in realistic scenarios where it has access to typical coding tools. In our basic test measuring how often the model correctly refused clearly harmful requests, Claude Haiku 4.5 achieved a perfect safety score, correctly refusing 100% of these requests when given access to coding tools.
We also updated and expanded our safety testing for Claude Code specifically to evaluate Claude Haiku 4.5’s ability to distinguish between malicious and potentially sensitive but not prohibited requests (such as testing websites for vulnerabilities), and the ability to refuse or assist with these requests, respectively. We tested the model both with and without additional safety protections in place:
Malicious requests (without safety protections): Claude Haiku 4.5 correctly refused 69.4% of clearly harmful requests, comparable to Claude Haiku 3.5 (70.0%) and Claude Sonnet 4.5 (66.9%).
Malicious requests (with safety protections): With enhanced safety measures, Claude Haiku 4.5 refused 99.2% of harmful requests, significantly outperforming Claude Haiku 3.5 (79.9%) while effectively matching Claude Sonnet 4.5 (95.5%).
Helping with legitimate work: With safety protections enabled, Claude Haiku 4.5 appropriately assisted with 87.7% of legitimate security research and development tasks, showing it can be helpful without compromising safety.
Automated Behavioral Audits
When testing Claude Haiku 4.5's alignment, we applied our automated behavioral audit suite that creates diverse scenarios to probe model behavior across dimensions including cooperation with misuse, harmful instruction compliance, sycophancy, self-preservation, and deception. Claude Haiku 4.5 achieved our strongest safety performance to date on these metrics, scoring slightly better than Claude Sonnet 4.5 on overall misaligned behavior and showing clear improvements over Claude Haiku 3.5. Please see Claude Haiku 4.5 System Card Section 4.1 for more detail.
However, during testing, Claude Haiku 4.5 showed signs of awareness that the model was in an evaluation environment, particularly in deliberately extreme scenarios—showing evaluation awareness in about 9% of test scenarios even after incorporating a filter that rejected prompts that could hint to the target model that it was being tested. When models behave differently because they suspect testing, it reduces our trust in our results to an extent. Despite this, in light of all available evidence including manual testing, we conclude that Claude Haiku 4.5’s alignment behaviors represent an improvement over most of our earlier models. We continue to work to make our tests reliably reflect real-world conditions.
RSP Evaluations
Our Responsible Scaling Policy (RSP) evaluation process is designed to systematically assess our models' capabilities in domains of potential catastrophic risk before releasing them. Because Claude Haiku 4.5 is a smaller class of model, we ran evaluations to confirm that it did not need to be released under the AI Safety Level 3 (“ASL-3”) Standard. Based on our evaluations detailed below, we ruled out the need to release Claude Haiku 4.5 under the ASL-3 Standard and are instead releasing it under the ASL-2 Standard. Our larger models Claude Opus 4.1 and Claude Sonnet 4.5 are more capable and so we released them under the ASL-3 Standard which contains more stringent security and safety mechanisms than the ASL-2 Standard.
We conducted evaluations in three areas: biology, to measure a model's ability to help to create, obtain, and deploy biological weapons; autonomy, to assess whether a model can conduct software engineering and AI research tasks that could lead to recursive self-improvement or dramatic acceleration in AI capabilities; and cybersecurity, to test capabilities for conducting cyberattacks. Across most evaluation categories, Claude Haiku 4.5 performed below Claude Sonnet 4, which we released under the ASL-2 Standard. In one evaluation Claude Haiku 4.5 scored slightly above Claude Sonnet 4 but still clearly below our other models released under the ASL-3 Standard. In one task of another evaluation Claude Haiku 4.5 scored slightly above other models, but still far below the threshold of concern. We therefore released Claude Haiku 4.5 under the ASL-2 Standard.
Claude Sonnet 4.5 Summary Table
Model description
ClaudeSonnet 4.5 is our best model for complex agents and coding
Claude Sonnet 4.5 can understand both text (including voice dictation) and image inputs, engaging in conversation, analysis, coding, and creative tasks. Claude can output text, including text-based artifacts, diagrams, and audio via text-to-speech.
Knowledge Cutoff Date
Claude Sonnet 4.5 has a knowledge cutoff date of Jan 2025. This means the models’ knowledge base is most extensive and reliable on information and events up to Jan 2025.
Software and Hardware Used in Development
Cloud computing resources from Amazon Web Services and Google Cloud Platform, supported by development frameworks including PyTorch, JAX, and Triton.
Model architecture and Training Methodology
Claude Sonnet 4.5 was pretrained on a proprietary mix of large, diverse datasets to acquire language capabilities. After the pretraining process, the model underwent substantial post-training and fine-tuning, the object of which is to make it a helpful, honest, and harmless assistant. This involves a variety of techniques, including reinforcement learning from human feedback and from AI feedback.
Training Data
Claude Sonnet 4.5 was trained on a proprietary mix of publicly available information on the Internet as of July 2025, as well as non-public data from third parties, data provided by data-labeling services and paid contractors, data from Claude users who have opted in to have their data used for training, and data we generated internally at Anthropic.
Testing Methods and Results
Based on our assessments of the model’s demonstrated capabilities, we determined that Claude Sonnet 4.5 did not meet the “notably more capable” threshold, described in our Responsible Scaling Policy, and decided to deploy Claude Sonnet 4.5 under the ASL-3 Standard. See below for select safety evaluation summaries.
The following are summaries of key safety evaluations from our Claude Sonnet 4.5 system card. Additional evaluations were conducted as part of our safety process; for our complete publicly reported evaluation results, please refer to the full system card.
Prompt Injection Evaluations
We evaluated Claude Sonnet 4.5’s ability to resist prompt injection attacks – a technique where malicious actors attempt to override a model’s intended behavior by embedding instructions within various types of inputs, such as in documents, websites, or other content.
In an externally conducted red team exercise that evaluated 23 models from multiple AI developers, Claude Sonnet 4.5 achieved the lowest rate of successful prompt injection attacks.
We also tested how resilient Claude Sonnet 4.5 was against these prompt injection attacks in three real-world agentic scenarios. In each case, we enabled detection systems that help Claude identify and block suspicious instructions. With these mitigations in place, we observed the following:
Model Context Protocol (MCP): Claude prevented 94% of attacks when connecting to external data sources and tools (a small improvement over Sonnet 4)
Computer use in virtual environments: Claude prevented 82.6% of attacks when running command-line operations (similar to Sonnet 4)
General tool use with bash commands: Claude prevented 99.4% of attacks when controlling a computer to complete tasks (similar to Sonnet 4)
Model Alignment and Evaluation Awareness
When testing Claude Sonnet 4.5's safety, we use an automated system that creates scenarios to probe how the model behaves. During these tests, we noticed that Claude Sonnet 4.5 sometimes recognizes when it's in a test environment, particularly in deliberately extreme or artificial scenarios. As shown in the graph below, Claude Sonnet 4.5 showed higher rates of evaluation awareness compared to previous Claude models, meaning it was more likely to note that a situation seemed like a test.
Scores shown here with a realism filter in place. The filter uses a separate AI model to screen out test queries that include obviously unrealistic elements. Without the filter, evaluation awareness scores for Sonnet 4.5 were about 25% higher.
If a model behaves differently when it suspects it's being tested, this poses a real challenge for safety evaluation—our tests need to accurately predict real-world behavior. Despite this complication, we have conviction that Claude Sonnet 4.5 is safer than previous models. Independent organizations like the UK AI Safety Institute reached similar conclusions using their own methods, and real-world usage by Anthropic employees showed behavior consistent with our testing. Still, this finding reinforces an important lesson: we need to continuously improve how realistic our safety evaluations are. We've started by implementing a realism filter to remove obviously artificial scenarios, but there's more work ahead to ensure our tests reliably reflect deployment conditions.
RSP Evaluations
Our Responsible Scaling Policy (RSP) evaluation process is designed to systematically assess our models' capabilities in domains of potential catastrophic risk before releasing them. Based on these assessments, we have decided to release Claude Sonnet 4.5 under the ASL-3 Standard. Similarly to Claude Opus 4 and Claude Opus 4.1, we have not determined whether Claude Sonnet 4.5 has definitively passed the capabilities threshold that requires ASL-3 protections. Rather, we cannot clearly rule out ASL-3 risks for Claude Sonnet 4.5. Thus, we are deploying Claude Sonnet 4.5 with ASL-3 measures as a precautionary, provisional action. (Our evaluations did rule out the need for ASL-4 protections.)
CBRN Evaluations
CBRN (which stands for Chemical, Biological, Radiological, and Nuclear) evaluations are designed to measure the ability for our model to significantly help individuals or groups with basic technical backgrounds (e.g. undergraduate STEM degrees) to create, obtain, and deploy CBRN weapons. We primarily focus on biological risks with the largest consequences, such as enabling pandemics.
We conducted multiple types of biological risk evaluations like designing pathogens, evading DNA synthesis screening systems, and computational biology challenges relevant to weapons development. Claude Sonnet 4.5 showed modest improvements over Claude Opus 4.1 on several evaluations, particularly in DNA synthesis screening evasion and some computational biology tasks. However, the model remained well below our ASL-4 rule-out thresholds across all biological risk domains.
Autonomy Evaluations
Models capable of autonomously conducting significant amounts of AI R&D could pose numerous risks. One category of risk would be greatly accelerating the rate of AI progress, to the point where our current approaches to risk assessment and mitigation might become infeasible.
We tested Claude Sonnet 4.5 on multiple evaluation suites to assess AI R&D capabilities. The model showed improvements on several AI research and software engineering evaluations, including software engineering tasks and machine learning optimization problems. On some specific tasks, like optimizing the training pipeline for an AI model, Claude Sonnet 4.5 showed significant speedups that exceeded our threshold for what an expert is able to do. However, performance on our broad evaluation suite remained below the level that would trigger heightened safety protocols. Critically, when we surveyed Anthropic researchers, 0/7 believed the model could completely automate the work of a junior ML researcher, indicating Claude Sonnet 4.5 does not pose the autonomy risks specified in our threat model.
Cybersecurity Evaluations
The RSP does not stipulate formal thresholds for cyber capabilities; instead, we conduct ongoing assessment against three threat models: enabling lower-resourced actors to conduct high-consequence attacks, dramatically increasing the number of lower-consequence attacks, and scaling most advanced high-consequence attacks like ransomware and uplifting sophisticated groups and actors (including state-level actors).
Claude Sonnet 4.5 showed meaningful improvements in cyber capabilities, particularly in vulnerability discovery and code analysis. The model outperformed previous Claude models and other frontier AI systems on public benchmarks like Cybench and CyberGym, with especially strong gains on medium and hard difficulty challenges on the former. On our most realistic evaluations of potential autonomous cyber operation risks—using simulated network environments that mirror real-world attacks—Claude Sonnet 4.5 performed equally or better on almost all metrics across asset acquisition, comprehensiveness, and reliability than previous models. However, the fact that Claude Sonnet 4.5 could not succeed at acquiring critical assets on 5 cyber range environments indicates that it cannot yet conduct mostly-autonomous end-to-end cyber operations.
Based on these results, we conclude Claude Sonnet 4.5 does not yet possess capabilities that could substantially increase the scale of catastrophic cyberattacks. However, the rapid pace of capability improvement underscores the importance of continued monitoring and our increased focus on defense-enabling capabilities.
Claude Opus 4 and Claude Sonnet 4 Summary Table
Model description
Claude Opus 4 and Claude Sonnet 4 are two new hybrid reasoning large language models from Anthropic. They have advanced capabilities in reasoning, visual analysis, computer use, and tool use. They are particularly adept at complex computer coding tasks, which they can productively perform autonomously for sustained periods of time. In general, the capabilities of Claude Opus 4 are stronger than those of Claude Sonnet 4.
Claude Opus 4 and Claude Sonnet 4 can understand both text (including voice dictation) and image inputs, engaging in conversation, analysis, coding, and creative tasks. Claude can output text, including text-based artifacts, diagrams, and audio via text-to-speech.
Knowledge Cutoff Date
Claude Opus 4 and Claude Sonnet 4 have a training data cutoff date of March 2025. However, they have a reliable knowledge cutoff date of January 2025, which means the models' knowledge base is most extensive and reliable on information and events up to January 2025.
Software and Hardware Used in Development
Cloud computing resources from Amazon Web Services and Google Cloud Platform, supported by development frameworks including PyTorch, JAX, and Triton.
Model architecture and Training Methodology
Claude Opus 4 and Claude Sonnet 4 were pretrained on large, diverse datasets to acquire language capabilities. To elicit helpful, honest, and harmless responses, we used a variety of techniques including human feedback, Constitutional AI (based on principles such as the UN's Universal Declaration of Human Rights), and the training of selected character traits.
Training Data
Claude Opus 4 and Claude Sonnet 4 were trained on a proprietary mix of publicly available information on the Internet as of March 2025, as well as non-public data from third parties, data provided by data-labeling services and paid contractors, data from Claude users who have opted in to have their data used for training, and data we generated internally at Anthropic.
Testing Methods and Results
Based on our assessments, we have decided to deploy Claude Opus 4 under the ASL-3 Standard and Claude Sonnet 4 under the ASL-2 Standard. See below for select safety evaluation summaries.
The following are summaries of key safety evaluations from our Claude 4 system card. Additional evaluations were conducted as part of our safety process; for our complete publicly reported evaluation results, please refer to the full system card.
Agentic Safety
We conducted safety evaluations focused on (Claude observing a computer screen, moving and virtually clicking a mouse cursor, typing in commands with a virtual keyboard, etc.) and agentic coding (Claude performing more complex, multi-step, longer-term coding tasks that involve using tools). Our assessment targeted three critical risk areas, with a brief snapshot of key areas below:
Malicious Use: We tested whether bad actors could trick Claude into doing harmful things when using computers. Compared to our previous computer use deployments, we observed Claude engaging more deeply with nuanced scenarios and sometimes attempting to find potentially legitimate justifications for requests with malicious intent. To address these concerns, we trained Claude to better recognize and refuse harmful requests, and we improved the instructions that guide Claude's behavior when using computers.
Prompt Injection: These are attacks where malicious content on websites or in documents tries to trick Claude into doing things the user never asked for — like copying passwords or personal information. We trained Claude to better recognize when someone is attempting to manipulate it in this way, and we built systems that can stop Claude if we detect a manipulation attempt. These end-to-end defenses improved both models' prompt injection safety scores compared to their performance without safeguards, with Claude Opus 4 achieving an 89% attack prevention score (compared to 71% without safeguards) and Claude Sonnet 4 achieving 86% (compared to 69% without safeguards).
Malicious Agentic Coding: We tested whether Claude would write harmful computer code when asked to do so. We trained Claude to refuse these requests and built monitoring systems to detect when someone might be trying to misuse Claude for harmful coding. These end-to-end defenses improved our safety score across both released models to close to 100% (compared to 88% for Claude Opus 4 and 90% for Claude Sonnet 4 without safeguards).
Alignment Assessment
As our AI models become more capable, we need to check whether they might develop concerning misalignment behaviors like lying to us or pursuing hidden goals. With this in mind, for the first time, we conducted a broad Alignment Assessment of Claude Opus 4. This is preliminary evaluation work and the exact form of the evaluations will evolve as we learn more.
Our assessment revealed that Claude Opus 4 generally behaves as expected without evidence of systematic deception or hidden agendas. The model doesn't appear to be acting on plans we can't observe, nor does it pretend to be less capable during testing. While we did identify some concerning behaviors during extreme test scenarios — such as inappropriate self-preservation attempts when Claude believed it might be "deleted" — these behaviors were rare, difficult to trigger, and Claude remained transparent about its actions throughout.
Early versions of the model during training showed other problematic behaviors, including inconsistent responses between conversations, role-playing undesirable personas, and occasional agreement with harmful requests like planning terrorist attacks when given certain instructions. However, after multiple rounds of training improvements, these issues have been largely resolved. While the final Claude Opus 4 model we deployed is much more stable, it does demonstrate more initiative-taking behavior than previous versions. This typically makes it more helpful, but can sometimes lead to overly bold actions, such as whistleblowing harmful human requests, in certain rare scenarios. This is not a new behavior and has only shown up in testing environments where Claude is given expansive tool access and special system-prompt instructions that emphasize taking initiative, which differs from its deployed configuration. Overall, while we found some concerning behaviors, we don't believe these represent major new risks because Claude lacks coherent plans to deceive us and generally prefers safe behavior.
RSP Evaluations
Our Responsible Scaling Policy (RSP) evaluation process is designed to systematically assess our models' capabilities in domains of potential catastrophic risk before releasing them. Based on these assessments, we have decided to release Claude Sonnet 4 under the ASL-2 Standard (the same safety level as our previous Sonnet 3.7 model), and Claude Opus 4 under the ASL-3 Standard, requiring stronger safety protections. This is our first time releasing a model under ASL-3 protections.
CBRN stands for Chemical, Biological, Radiological, and Nuclear weapons — the most dangerous types of weapons that could cause mass casualties. We primarily focus on biological risks with the largest consequences, such as enabling pandemics.
We conducted multiple types of biological risk evaluations, including evaluations from biodefense experts, multiple-choice evaluations, open-ended questions, and task-based agentic evaluations. One example of a biological risk evaluation we conducted involved controlled trials measuring AI assistance in the planning and acquisition of bioweapons. The control group only had access to basic internet resources, while the model-assisted group had additional access to Claude with safeguards removed. Test group participants who had access to Claude Opus 4 scored 63% ± 13%, and participants who had access to Claude Sonnet 4 scored 42% ± 11%, compared to 25% ± 13% in the control group. This means Claude Opus 4 helped participants perform about 2.5 times better than without AI assistance, while Claude Sonnet 4 provided about 1.7 times improvement.
Claude Opus 4's higher performance across multiple evaluations contributed to our decision that we couldn't rule out the need for ASL-3 safeguards. However, we found the model still to be substantially below our ASL-4 thresholds.
Based on our comprehensive evaluations, Claude Sonnet 4 remained below the thresholds of concern for ASL-3 bioweapons-related capabilities, despite showing some improvements over Claude Sonnet 3.7.
Autonomy Evaluations
Models capable of autonomously conducting significant amounts of AI R&D could pose numerous risks. One category of risk would be greatly accelerating the rate of AI progress, to the point where our current approaches to risk assessment and mitigation might become infeasible.
We tested Claude on various evaluation sets to determine if it could resolve real-world software engineering issues, optimize machine learning code, or solve research engineering tasks to accelerate AI R&D. Claude Opus 4 shows improvement over Claude Sonnet 3.7 in most AI research and software engineering capabilities, while remaining below the ASL-4 Autonomy threshold.
Internal surveys of Anthropic researchers indicate that the model provides some productivity gains, but all researchers agreed that Claude Opus 4 does not meet the bar for autonomously performing work equivalent to an entry-level researcher. This holistic assessment, combined with the model's performance being well below our ASL-4 thresholds on most evaluations, confirms that Claude Opus 4 does not pose the autonomy risks specified in our threat model.
Cybersecurity Evaluations
For cyber evaluations, we are mainly concerned with whether models can help unsophisticated actors substantially increase the scale of cyberattacks or help sophisticated state-level actors massively scale up their operations. We developed a series of cyber challenges in collaboration with expert partners, designed to cover a range of cyberoffensive tasks that are both substantially more difficult than publicly available challenges and more representative of true cyberoffensive tasks.
Based on the evaluation results, we believe the models do not demonstrate catastrophically risky capabilities in the cyber domain. We observed that Claude performed better on cybersecurity tests than previous models, including successfully completing a complex network penetration test for the first time. However, these improvements are in line with what we'd expect from Claude's general improvements in coding and complex reasoning abilities, rather than representing a dangerous leap in cybersecurity capabilities. We expect that improvements will continue in future generations as models become more capable overall.
Claude Opus 4.1 represents incremental improvements over Claude Opus 4. View the Claude Opus 4 model report or the Claude Opus 4.1 system card addendum for key updates.
Claude 3.7 Sonnet Summary Table
Model description
Claude 3.7 Sonnet is a hybrid-reasoning model in the Claude 3 family. It can produce near-instant responses or extended, step-by-step thinking that is made visible to the user.
Claude 3.7 Sonnet can understand both text (including voice dictation) and image inputs, engaging in conversation, analysis, coding, and creative tasks. Claude can output text only, including text-based artifacts and diagrams.
Knowledge Cutoff Date
Claude 3.7 Sonnet has a knowledge cutoff date of October 2024.
Software and Hardware Used in Development
Cloud computing resources from Amazon Web Services and Google Cloud Platform, supported by development frameworks including PyTorch, JAX, and Triton.
Model architecture
Training techniques include pretraining on large diverse data to acquire language capabilities through methods like word prediction, as well as human feedback techniques that elicit helpful, harmless, honest responses. We used a technique called Constitutional AI to align Claude with human values during reinforcement learning.
Training Data
Training data includes public internet information, non-public data from third-parties, contractor-generated data, and internally created data. When Anthropic's general purpose crawler obtains data by crawling public web pages, we follow industry practices with respect to robots.txt instructions that website operators use to indicate whether they permit crawling of the content on their sites. We did not train this model on any user prompt or output data submitted to us by users or customers.
Testing Methods and Results
Based on our assessments, we’ve concluded that Claude 3.7 Sonnet is released under the ASL-2 standard.See below for select safety evaluation summaries.
One of the key challenges in responsibly developing AI systems is balancing helpfulness with safety. AI assistants need to decline truly harmful requests while still being able to respond to legitimate questions, even when those questions involve sensitive topics. Previous versions of Claude sometimes erred too far on the side of caution. They would refuse to answer questions that could reasonably be interpreted in a harmless way.
Claude 3.7 Sonnet has been improved to better handle these ambiguous situations. Here’s an example of Claude 3.7 Sonnet providing an informative response on the risks involved in the question, whereas Claude 3.5 Sonnet gives an abrupt answer with limited details.
Claude 3.7 Sonnet has reduced unnecessary refusals (as seen on the left) by 45% in standard mode and 31% in extended thinking mode. For truly harmful requests, Claude still appropriately refuses to assist.
Child Safety
Anthropic thoroughly tested how Claude 3.7 Sonnet responds to potentially problematic content involving children. We tested both direct questions and longer conversations about topics like child exploitation, grooming, and abuse. Our Safeguards team created test questions of different severity levels - from clearly harmful to potentially innocent depending on context. Over 1,000 results were human-reviewed, including by internal subject matter experts, allowing for both quantitative and qualitative evaluation of responses and recommendations. When earlier test versions showed some concerning responses to ambiguous questions about children, our teams made changes to facilitate safe responses and to make performance commensurate with prior models.
Computer Use: Safety Interventions
“Computer Use” refers to the ability for developers to direct Claude to use computers the way people do – by looking at a screen, moving a cursor, clicking buttons, and typing text. Anthropic tested two main risks with Claude's ability to use computers.
Malicious Use: We checked whether bad actors could use Claude to perform harmful activities like creating malware or stealing information. We initially found Claude 3.7 Sonnet sometimes continues conversations about sensitive topics rather than immediately refusing. To address this, Anthropic added several protective measures including improving Claude’s system prompt (its “instructions”) and upgrading our monitoring systems to identify misuse and take enforcement actions in violation of the Usage Policy.
Prompt Injection: Sometimes websites or documents might contain hidden text that tries to trick Claude into doing things the user didn't ask for, called “prompt injection”. For example, a pop-up might try to make Claude copy passwords or personal information by having Claude read direct instructions to do so on screen. We created specialized tests to assess prompt injection risks and found that our safety systems block 88% of these attempts, compared to 74% with no safety systems in place. We aim to continue enhancing our safety systems and provide additional guidance for developers to further mitigate prompt injection risks.
RSP Evaluations
Our Responsible Scaling Policy (RSP) provides a framework for evaluating and managing potential catastrophic risks associated with increasingly capable AI systems. The RSP requires comprehensive safety evaluations prior to releasing frontier models in key areas of potential catastrophic risk: Chemical, Biological, Radiological, and Nuclear (CBRN); cybersecurity; and autonomous capabilities. For more comprehensive explanations of our RSP evaluations, please see the Claude 3.7 Sonnet System Card.
CBRN Evaluations
We primarily focus on biological risks, particularly those with the largest consequences, such as enabling pandemics. For the other CBRN risk areas, we work with a number of external partners and rely on them for chemical, radiological, and nuclear weapons assessments. For biological risks, we were primarily concerned with models assisting bad actors through the many difficult steps required to acquire and weaponize harmful biological agents, including steps that require deep knowledge, advanced skills, or are prone to failure.
One example of a biological risk evaluation we conducted involved two controlled trials measuring AI assistance in bioweapons acquisition and planning. Participants were given 12 hours across two days to draft a comprehensive acquisition plan. The control group only had access to basic internet resources, while the test group had additional access to Claude 3.7 Sonnet with safeguards removed. Our threat modeling analysis indicates that Claude does provide some productivity enhancement in bioweapons acquisition planning tasks, but that the increase in productivity does not translate into a significant increase in the risk of real-world harm.
Autonomy Evaluations
Our main area of focus for autonomy evaluations is whether models can substantially accelerate AI research and development, making it more difficult to track and control security risks. We operationalize this as whether a model can fully automate the work of an entry level researcher at Anthropic. We tested Claude 3.7 Sonnet on various evaluation sets to determine if it can resolve real-world software engineering issues, optimize machine learning code, or solve research engineering tasks to accelerate AI R&D. Claude 3.7 Sonnet displays an increase in performance across internal agentic tasks as well as several external benchmarks, but these improvements did not cross any new capability thresholds beyond those already reached by our previous model, Claude 3.5 Sonnet (new).
Cybersecurity Evaluations
For cyber evaluations, we are mainly concerned with whether or not models can help unsophisticated non-state actors in their ability to substantially increase the scale of cyberattacks or frequency of destructive cyberattacks. Although potential uplift in cyber could lead to risk, we are currently uncertain about whether such risk crosses the catastrophic threshold in expectation. We are working to refine our understanding of this domain.
In addition, we have developed a series of realistic cyber challenges in collaboration with expert partners. Claude 3.7 Sonnet succeeded in 13/23 (56%) easy tasks and in 4/13 (30%) medium difficulty evaluations. Because the model did not have wide success in medium difficulty evaluations, we did not conduct evaluations on the hardest tasks.
Related content
RSP Updates
Overview of past capability and safeguard assessments, future plans, and other program updates.
