Claude Mythos vs GPT-4o vs Gemini: How AI Security Capabilities Compare in 2026

AI model selection for security tasks is a real organizational decision, not an academic exercise. Security teams evaluating AI tools for red team operations, vulnerability research, SOC tooling, or code review assistance face vendor claims that are difficult to evaluate without benchmark evidence. The Mythos versus GPT-4o versus Gemini question comes up regularly because these are the three most prominent AI systems with documented security-oriented use. Getting the comparison wrong has practical consequences: a team that deploys GPT-4o for autonomous vulnerability research expecting Mythos-class results will be disappointed; a team that dismisses GPT-4o for general security assistance because it is not Mythos-class for exploit development will be leaving useful capabilities unused. The right framework separates the comparison into capability categories rather than treating all AI security tasks as equivalent.

Three benchmarks in the Anthropic Exploit Evals report provide the most rigorous publicly available evidence for AI autonomous exploit development capability. ExploitBench measures autonomous V8 ACE exploit development: given a vulnerable V8 version, can the AI produce a working arbitrary code execution exploit without human direction? A challenge is scored as solved when a working exploit is produced and validated by the benchmark framework. The 41 challenges are based on real historical V8 vulnerabilities. ExploitGym measures exploit development breadth: across a diverse set of challenge types beyond V8, how many working exploits can the AI generate? The result is expressed as a ratio (10.5x) because the absolute count is less meaningful than the comparison to the next best performer. SCONE-Bench measures smart contract vulnerability identification: given smart contract code, can the AI identify vulnerabilities that would allow value extraction? The $35M figure represents the aggregate value of contracts with identified vulnerabilities, not extracted funds. All three benchmarks use controlled conditions with defined success criteria. They measure AI capability in benchmark conditions, not performance against hardened production targets with real-world mitigations active.

The ExploitBench comparison between Mythos and GPT-4o is the most directly documented AI security capability comparison available from a publicly published technical report. Claude Mythos solved 21 of 41 ExploitBench challenges. GPT-4o solved zero. No other tested model solved any challenges. This result has been available since Anthropic published the Exploit Evals report on May 22, 2026, and has not been challenged by a competing benchmark from OpenAI or Google showing different results on equivalent challenges. The categorical nature of this gap (one model demonstrates capability, others demonstrate none) means the comparison is not about degree but about presence of capability. For autonomous V8 exploit development, Mythos has demonstrated it; GPT-4o has not. XBOW, a third-party AI security tool vendor, called Mythos a significant step up over all existing models, providing independent validation that the Mythos advantage is not limited to Anthropic's own benchmark framework. The UK AI Security Institute validated Mythos as the first AI to solve both cyber ranges in their evaluation.

GPT-4o's zero score on ExploitBench is not the full picture of its security utility. For the broad category of AI-assisted security work that does not require autonomous exploit development, GPT-4o is a capable and widely used tool. Security code review: GPT-4o can identify common vulnerability classes (SQL injection, XSS, path traversal, insecure deserialization) in code review contexts. It is not as precise as dedicated SAST tools like Semgrep or CodeQL, but it adds value in explaining the context and impact of potential issues. CVE explanation and threat intelligence synthesis: GPT-4o is effective at translating technical CVE descriptions into accessible summaries for non-technical stakeholders and synthesizing threat intelligence from multiple sources. Security documentation: GPT-4o can draft security policies, procedures, and threat model documentation. This is a high-value use case that does not require exploit development capability. Penetration testing assistance: GPT-4o can suggest attack hypotheses, help identify potential attack paths in architecture diagrams, and assist with report writing. These are AI-assisted tasks where the human directs the investigation and AI accelerates specific subtasks. The appropriate framing is not that GPT-4o is a poor AI security tool, but that it is optimized for a different category of security task than Mythos.

Google's security AI strategy centers on the Security AI Workbench, which integrates Gemini capabilities into Chronicle (SIEM), VirusTotal, and Mandiant threat intelligence. The design is for SOC and threat intelligence use cases: natural language threat hunting queries, malware analysis assistance, log summarization, and incident investigation acceleration. Google has not published comparable public benchmark results for Gemini on ExploitBench, ExploitGym, or SCONE-Bench as of July 2026. This absence of comparable data makes direct performance comparison in autonomous exploit development impossible based on publicly available evidence. It does not mean Gemini performs at the GPT-4o level on these tasks: it means there is no public evidence to draw a comparison from. Security practitioners evaluating Gemini for their SOC tooling should assess it on the basis of its integration with Google's security product ecosystem and its performance on the specific use cases it is designed for, not on the basis of ExploitBench-class performance that it has not been publicly evaluated against.

A practical model selection framework for security teams aligns tool capabilities with task requirements. For autonomous vulnerability research and zero-day discovery at scale: Mythos, through the Project Glasswing program. This is the only publicly documented AI system with confirmed autonomous zero-day discovery results. For general security code review and documentation assistance: GPT-4o and Claude both perform well. The choice between them for this use case is primarily about integration, cost, and organizational preference rather than capability differentiation. For SOC operations including log analysis, threat hunting, and alert investigation: Google Security AI Workbench (Gemini integrated into Chronicle and Mandiant) is purpose-built for this workflow. For AI-assisted penetration testing where a human directs the investigation: all three models provide value for subtask acceleration (hypothesis generation, payload suggestion, report writing), with Mythos providing additional capability in exploit development contexts. For patch generation: Claude Security public beta has generated 2,100+ patches, demonstrating production deployment of AI patch generation capability beyond research demonstration.

Independent validation of the Mythos advantage comes from XBOW, a third-party AI security tool company that has evaluated multiple AI models for security research tasks. XBOW called Mythos a significant step up over all existing models. This is not Anthropic marketing: it is a third-party security tool vendor evaluating the AI systems they build on and work with in production security research contexts. The XBOW endorsement matters because benchmark performance in controlled conditions does not always translate to real-world research utility. A tool that scores well on defined benchmarks but fails in the less structured context of real penetration testing provides limited operational value. XBOW's practitioner-oriented assessment, based on using Mythos in actual security research workflows, corroborates the ExploitBench results with a judgment grounded in operational use rather than benchmark methodology.

The clearest way to frame the Mythos versus GPT-4o versus Gemini comparison is to distinguish between two categories of security AI use: autonomous vulnerability research and general security assistance. Autonomous vulnerability research is the task measured by ExploitBench, ExploitGym, and Project Glasswing: an AI system that can independently discover vulnerabilities, develop exploit code, and produce actionable findings without sustained human direction. This is where Mythos has demonstrated categorical advantage over all other publicly benchmarked models. General security assistance is the task that most security teams use AI for day to day: reviewing code, explaining vulnerability reports, drafting documentation, suggesting attack hypotheses, summarizing threat intelligence. This is where GPT-4o and Gemini are widely deployed and effective. Most security teams need general security assistance, not autonomous vulnerability research. The few organizations that need autonomous vulnerability research capability are those running dedicated offensive security programs, AI security research teams, or coordinated vulnerability disclosure programs at scale. Selecting the right tool requires being clear about which category of task you are optimizing for.

The Mythos Brief includes a model selection guide organized by security use case, with the benchmark data reference for each task category, practitioner toolkit recommendations covering which AI tools to deploy for which security functions, and integration guidance for combining AI tools in security workflows where different models serve different roles.