Claude Mythos Preview and the 93.9% SWE-bench score, explained

What the 93.9% does not mean

The 93.9% figure should not be read as a global grade for Claude Mythos Preview. A software benchmark does not, by itself, measure general reasoning, safety, availability, operating cost, or performance on tasks that do not look like writing, reviewing, or modifying code .

For a fair comparison, the rule of thumb is simple: compare models on the same benchmark under similar conditions. If one model gets file access, code execution, test feedback, and multiple attempts, while another is tested without those tools, the comparison can become misleading .

Reported Claude Mythos Preview benchmarks

Software and cybersecurity scores are different signals

Claude Mythos Preview’s cybersecurity results belong in a separate category from SWE-bench. One source reports 83.1% for Mythos Preview versus 66.6% for Claude Opus 4.6 on cybersecurity capability benchmarks . Another says Mythos achieved 100% on Cybench, described as a benchmark for cybersecurity challenges .

The Anthropic-linked materials available here also focus heavily on security. Anthropic Red Team published an assessment of Claude Mythos Preview’s cybersecurity capabilities, while Project Glasswing includes work on identifying vulnerabilities and exploits with the model . That context may be highly relevant for security teams, but it should not be blended with SWE-bench as though all of these results were one combined score.

How to use the number in a real evaluation

If your use case is a coding agent that works inside repositories, edits code, runs tests, and improves its solution over several attempts, the 93.9% SWE-bench result is the best starting point . If your use case is vulnerability analysis, secure code review, or exploit research, the cybersecurity evaluations and Anthropic’s security-focused materials are the more relevant context .

The short answer is: Claude Mythos Preview is reported at 93.9% on SWE-bench . The more rigorous answer is narrower: that is a strong signal for software tasks under specific evaluation conditions, not automatic proof that the model is superior in every domain.