An AI model programmed nonstop for 19 days on a single MirrorCode task that cost $2,600 to run
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Original coverage: The Decoder ↗·How Modelwire adds context
The development
Epoch AI's MirrorCode benchmark reveals a critical frontier in AI capability: reverse-engineering complete software systems from behavior alone. Claude Opus 4.7 achieved a 56 percent solve rate, reconstructing a 16,000-line toolkit in 14 hours, but the benchmark exposes a hard ceiling on complex tasks where all tested models fail. The $2,600 cost and 19-day runtime on individual problems signal both the computational intensity of this capability class and the gap between narrow wins and production-grade code synthesis. This matters for security teams, software auditing, and anyone tracking whether LLMs can move beyond pattern completion into genuine program reconstruction.
Modelwire’s AI-generated summary of coverage from The Decoder.
Modelwire analysis
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The more telling number isn't the 56 percent solve rate on simpler tasks but the hard zero: no model tested could complete the benchmark's most complex problems at any price, which means the $2,600 figure represents a ceiling being probed, not a floor being optimized.
This is largely disconnected from recent activity in our archive, as we have no prior coverage of MirrorCode, Epoch AI's benchmarking work, or the broader software-synthesis capability class. That absence is itself worth noting: most AI benchmark coverage gravitates toward reasoning and language tasks, and infrastructure-level evaluations like code reconstruction tend to surface later in the coverage cycle, often only after a security or audit use case forces the issue.
Watch whether Epoch AI releases a versioned leaderboard that tracks model-over-model progress on the failing hard tasks specifically. If a future model cracks even one of those zero-score problems at comparable cost, that is the signal that the ceiling is moving rather than fixed.
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MentionsEpoch AI · MirrorCode · Claude Opus 4.7 · The Decoder
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