GPT-5.5-Cyber
GPT-5.5-Cyber is an advanced cybersecurity-focused AI model designed for verified defenders working on authorized security research, vulnerability discovery, and remediation. The model pairs stronger cyber capabilities with more permissive behavior for specialized workflows that require deep analysis across complex software environments. It can help identify security-relevant components, trace vulnerable code paths, validate likely issues in controlled settings, develop and test patches, and prepare evidence for human review. GPT-5.5-Cyber is built to support the full remediation loop rather than simply generating more findings. The model shows stronger benchmark performance than GPT-5.5 on CyberGym, ExploitGym, and SEC-bench Pro, reflecting improvements in vulnerability reproduction, exploit reasoning, and long-horizon security tasks. GPT-5.5-Cyber is intended for advanced, authorized cybersecurity work with verification, monitoring, scoped controls, and review.
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Gemini 3.5 Flash Cyber
Gemini 3.5 Flash Cyber is a specialized cyber-focused model built on Gemini 3.5 Flash and fine-tuned to find, validate, and fix cybersecurity vulnerabilities efficiently at scale. It is designed for defensive security workflows where organizations need to identify critical weaknesses faster and generate reliable patches before those issues can be exploited. Flash’s combination of performance and efficiency makes it a strong foundation for scanning code, reasoning about security flaws, validating whether findings are real, and proposing targeted remediations across large software environments. Within CodeMender, multiple Gemini 3.5 Flash Cyber agents work together and combine their findings into a single report, helping the system investigate vulnerabilities from different angles and improve the quality of the final result. This coordinated agent setup delivers competitive frontier performance on CyberGym, a benchmark for evaluating cybersecurity capabilities.
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Fugu Cyber
Fugu Cyber is a specialized multi-agent orchestration model purpose-built for modern cyber defense. It behaves like a single model through one API endpoint, but dynamically coordinates specialized agents to solve complex, multi-step security tasks without depending on one model provider. It focuses on two core defense workflows, analyzing complex codebases to verify real-world vulnerabilities and translating raw cyber threat intelligence into working detection rules. On CyberGym, which evaluates vulnerability analysis and verification, Fugu Cyber achieved an 86.9% success rate; on CTI-REALM, which measures detection-rule generation from threat reports, it reached 72.1%, placing it alongside leading cyber-focused frontier models. Fugu Cyber is intended to work as the reasoning engine inside broader security systems rather than as a standalone solution.
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Muse Spark 1.2
Muse Spark 1.2 is Meta’s coding-focused model update designed to power Muse Code and improve software engineering workflows. The model is built for code generation, complex debugging, codebase understanding, long-horizon development tasks, and end-to-end developer workflows. Muse Spark 1.2 was co-trained with Muse Code to improve performance inside the terminal coding agent environment. It supports planning, goal conditioning, context compaction, subagent coordination, and iterative coding workflows across large repositories. The model was trained with expanded coding compute, diverse development environments, self-improvement loops, and long-running engineering tasks. Built for AI developers and software teams, Muse Spark 1.2 helps agents plan, write, validate, debug, and optimize code with greater autonomy.
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