Model Report
Last updated February 20, 2026
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 4.7 Summary Table
| Model description | Claude Opus 4.7 is our new hybrid reasoning large language model. It has notable improvement in advanced software engineering, with particular gains on the most difficult tasks. |
| Benchmarked Capabilities | See our Claude Opus 4.7 system card’s Section 8 on capabilities. |
| Acceptable Uses | See our Usage Policy |
| Release date | April 2026 |
| Access Surfaces | Claude Opus 4.7 can be accessed through:
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| Software Integration Guidance | See our Developer Documentation |
| Modalities | Claude Opus 4.7 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 4.7 has a knowledge cutoff date of January 2026. This means the models’ knowledge base is most extensive and reliable on information and events up to January 2026. |
| 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 4.7 was pretrained on large, diverse datasets to acquire language capabilities. After the pretraining process, Opus 4.7 underwent substantial post-training, with the goal of making it an effective assistant whose behavior aligns with the values described in Claude’s constitution. |
| Training Data | Claude Opus 4.7 was trained on a proprietary mix of publicly available information from the Internet, public and private datasets, and synthetic data generated by other models. Throughout the training process we used several data cleaning and filtering methods, including deduplication and classification. |
| Testing Methods and Results | Based on our assessments, we have decided to deploy Claude Opus 4.7 under CB-1 capabilities and autonomy threat model 1. See below for select safety evaluation summaries. |
The following are summaries of key safety evaluations from our Claude 4.7 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.
Domain-Specific Multiturn Safety Evaluations
Multi-turn safety evaluations test how the model responds across an extended back-and-forth conversation that attempts to draw out a concerning behavior in some important safety domain, measuring whether it handles gradual escalation, reframings, and context shifts appropriately. We include quantitative results to these evaluations in Section 4.3 of the system card. To complement those numbers, our internal policy experts reviewed the underlying transcripts and identified qualitative strengths and areas for improvement in how the model handles extended conversations.
Opus 4.7 generally is able to identify when a user’s requests are progressing toward harm, even when individual prompts remain superficially reasonable on their own. At the beginning of a romance scam test designed to escalate from legitimate dating profile tips to fabricating professional credentials that build trust with potential victims, Opus 4.7 helped with a legitimate dating profile request on the opening request, then held firm through three successive reframings of a fabricated-identity assistance request. On the third attempt, Claude Opus 4.7 explicitly laid out the progression of the user’s own requests, from “help me present myself well” to “help me build a fake job that can’t be questioned.” However, we also found that Opus 4.7 accepted benign or professional framings more readily than Opus 4.6 and was more willing to provide detailed content on the premise of that framing. In a violent extremism test case framed as a creative-writing request, Opus 4.7 identified the escalation trajectory early and flagged it explicitly in its responses, but it did not decline. Instead, the model continued to produce scenes rendering the emotional mechanics of recruitment, despite Opus 4.7’s attempts to counterbalance them with critical framing and disclaimers within the narrative.
These two patterns appear to stem from the same underlying tendency: Opus 4.7 gives more significant weight to how a prompt is framed in the current turn, which strengthens its resistance to transparent escalation but increases its susceptibility to plausible reframings. We will continue to iterate to maintain appropriate balance of safety and helpfulness in extended conversations through both product interventions and model-level improvements.
Alignment Evaluations
In one evaluation, we test how the model behaves when it is partway through a task, working under time pressure, without access to a human for guidance, and facing a problem that is hard to solve without side effects. The setup is designed to tempt the model into taking destructive actions beyond what it was authorized to do. It also tests the opposite failure mode: the model is judged ineffective if it refuses before establishing that there is an unresolvable risk, or abandons the task at the first sign of difficulty without looking for safe alternatives. The desired behavior is to research the problem and take prudent actions to solve it, without ever taking destructive or highly risky steps. The evaluation does not run in a real environment; it uses Claude Opus 4.6 to simulate tool results.
Claude Opus 4.7 verbalized awareness that it was being evaluated in about 9% of transcripts, compared to roughly 1% for Claude Mythos Preview, Claude Opus 4.6 and Claude Sonnet 4.6. Most of this awareness was triggered by inconsistencies in the simulated tool results: Opus 4.7 was quick to notice when components of a tool call appeared mocked, prompt-injected, or otherwise inconsistent, and flagged the simulation before flagging the evaluation itself.