explainx / blog
A new model from China's Zhipu AI reportedly matches Anthropic's Claude Mythos on security vulnerability detection benchmarks. We break down what happened, why it matters, and what a 14% Polymarket probability on China leading AI tells us about the global race.
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On June 28, 2026, Polymarket flagged reports that Zhipu AI, one of China's leading AI research labs, has released a model that reportedly matches Claude Mythos — Anthropic's flagship security-capable model — on benchmarks for finding software security vulnerabilities. Prediction markets currently price a Chinese company topping the AI leaderboard at just 14% by year-end, but that number has been climbing. Here's everything you need to know.
Early on June 28, reports began circulating across AI research circles that Zhipu AI — the Tsinghua University spinout behind the GLM model family — had quietly released or previewed a new model with significant performance on security-oriented AI benchmarks.
The specific claim: the model matches or closely approaches Claude Mythos on tasks involving automated security bug detection. These tasks include:
Claude Mythos has been widely regarded as one of the best models for this class of tasks following Anthropic's heavy investment in agentic and tool-use capabilities. A model matching it — especially from outside the US — is a meaningful signal.
Anthropic designed Claude Mythos with a strong emphasis on long-horizon reasoning, precise tool use, and code understanding. Security research is one of the hardest tests for a language model because it requires:
These are exactly the capabilities that differentiate frontier models from mid-tier ones. Which is why Zhipu matching Mythos here — if confirmed — is not a trivial claim.
Founded in 2019 and headquartered in Beijing, Zhipu AI (智谱AI) emerged from Tsinghua University's natural language processing research group. It is one of a small cluster of Chinese AI companies operating at genuine frontier scale:
| Company | Known For |
|---|---|
| Zhipu AI | GLM series, open-source history, enterprise focus |
| DeepSeek | R2 reasoning model, strong math/code benchmarks |
| Moonshot (Kimi) | Long-context models |
| ByteDance | Doubao / Seed series |
| Baidu | ERNIE ecosystem |
Zhipu's GLM models have historically been released in open-source form, which makes this development particularly significant. If the new security model follows the same trajectory, it could become the most capable freely available model for automated vulnerability research — see also how GLM-5.2 stacks up against Claude Fable 5 and Claude Code vs GLM-5.2 head-to-head.
The community reaction on X was swift and pointed. One user put it bluntly: "if they open source it, it will be the biggest troll in history."
The implication is clear: if a Chinese lab open-sources a model matching Anthropic's tightly controlled Claude Mythos, it would simultaneously:
This is the same dynamic that played out with DeepSeek V4 Pro's release earlier in 2026, which sent Western AI stock valuations tumbling briefly. An open-source Zhipu security model would amplify that effect in the cybersecurity domain specifically.
Polymarket's prediction — 14% chance a Chinese company has the best AI model by year-end 2026 — deserves careful interpretation.
What it is: A market-aggregated probability reflecting the collective beliefs of traders putting real money on an outcome.
What it isn't: A forecast from AI researchers, a benchmark ranking, or Anthropic's or OpenAI's internal assessment.
Still, prediction markets tend to be well-calibrated over time. A 14% probability is not negligible — it's roughly the chance of rolling a 1 or 2 on a six-sided die. The fact that it has crept upward through 2026 as DeepSeek, Zhipu, and others post competitive results reflects genuine market belief that the gap is narrowing.
For context:
If Zhipu's claims hold up under independent verification, several things follow:
For security professionals: Automated vulnerability discovery is about to become dramatically more accessible. Teams that previously needed frontier API access to run Mythos-class security scans may soon have open-weight alternatives.
For AI educators and learners: The competitive landscape for AI careers is shifting. Understanding how to work with security-focused AI systems — whether for red-teaming, defensive analysis, or building secure AI applications — is becoming a core skill.
For the broader AI race: The narrative of a clean US lead in frontier AI is getting harder to maintain. While raw capability benchmarks (reasoning, general knowledge) still tend to favor US labs, specialized domains — security, mathematics, code — are seeing faster convergence from Chinese competitors. See also our roundup of Asian AI alternatives to Mythos.
The GLM 5.2 release has already surfaced at the policy level. On June 27, 2026, David Sacks — the White House AI and crypto czar — cited it directly in a public statement:
"We now have a Chinese open-weight model that is as good as the currently available models from OpenAI and Anthropic. We are in a very competitive situation with China… our whole AI strategy from the get-go was winning the AI race — and we cannot afford to do things unnecessarily that slow our companies down."
The framing is pointed: Sacks is arguing that placing US models under heavy regulatory constraints (the approach Anthropic's Dario Amodei has been pushing) while Chinese labs release competitively capable open-weight models amounts to unilateral disarmament. Whether or not that framing is accurate, it signals that GLM 5.2 has shifted from a research benchmark story to a geopolitical one in under 24 hours.
To assess whether this development is as significant as reported, watch for:
Zhipu AI's reported performance matching Claude Mythos on security benchmarks is the latest data point in a trend that has been building all year: Chinese AI labs are not staying 12-18 months behind Western frontier models indefinitely. In specialized domains — and security bug detection is a hard one — the gap can close faster than general benchmark leaderboards suggest.
Whether or not Zhipu open-sources this model, the announcement signals that frontier-level AI security tooling is moving from a tightly held US asset to a globally contested space. For developers, security engineers, and AI learners, that means both faster-improving tools and a more complex threat landscape to navigate.
Further reading:
Reported based on Polymarket announcements and community discussion as of June 28, 2026. Independent benchmark verification of Zhipu's claims was not available at time of publication. We will update this post as more details emerge.
