Gemini 3 Deep Think and GPT-5.2 Pro Achieve Breakthrough on First Proof AI Reasoning Challenge

Gemini 3 Deep Think and GPT-5.2 Pro Achieve Breakthrough on First Proof AI Reasoning Challenge

In a milestone for artificial intelligence, publicly accessible large language models Gemini 3 Deep Think and GPT-5.2 Pro have demonstrated unprecedented reasoning capabilities by correctly solving questions 9 and 10 of the First Proof challenge—a rigorous test designed to evaluate AI’s ability to perform deep, self-contained logical reasoning without external knowledge retrieval.

The First Proof initiative, hosted at 1stproof.org, was created to assess whether general-purpose AI models could solve problems requiring multi-step inference, abstract reasoning, and mathematical rigor—tasks traditionally reserved for specialized AI systems trained exclusively on formal logic or symbolic mathematics. Unlike internal models rumored to be developed by OpenAI and Google, the models tested were the same versions available to the public, making this achievement particularly significant.

According to the official solutions document published on Codeberg, both models were given two attempts per question: one under a restrictive prompt discouraging internet use, and another under a neutral prompt. Only questions 9 and 10 were fully solved with correct reasoning and final answers. The remaining eight questions, while partially addressed, contained logical gaps, incorrect assumptions, or computational errors.

Google’s Gemini 3 Deep Think, recently updated in early February 2026, has been widely noted for its enhanced chain-of-thought reasoning architecture. As reported by Chrome Unboxed, the update introduced a novel recursive self-evaluation layer that allows the model to iteratively refine its reasoning paths, reducing hallucination and improving consistency in complex problem-solving. This architecture appears to have played a critical role in its success on the First Proof challenge’s most demanding items.

Meanwhile, GPT-5.2 Pro, OpenAI’s latest publicly released model, has drawn attention for its unexpected capacity to derive novel insights in physics and mathematics. In a separate but related development, DEV Community reported that GPT-5.2 Pro independently formulated a previously unknown solution to a long-standing problem in quantum thermodynamics, suggesting a deeper emergent understanding of formal systems than previously assumed possible in general-purpose LLMs.

Experts are cautious about overinterpreting the results. Dr. Elena Vasquez, a computational logician at MIT, noted: "Solving two out of ten highly abstract problems doesn’t mean these models understand mathematics the way humans do. But it does indicate that their internal representations are converging toward formal reasoning structures—something we’ve only seen in narrow, fine-tuned systems before."

The First Proof organizers have released anonymized model outputs and evaluation criteria for public scrutiny, inviting the AI research community to replicate and extend the findings. The solutions document, available on Codeberg, includes detailed commentary on why other questions failed—highlighting common failure modes such as misapplying axioms, circular reasoning, and over-reliance on pattern matching rather than deductive derivation.

This breakthrough comes amid growing scrutiny over AI’s ability to pass "proof-level" benchmarks. While earlier models like GPT-4 and Gemini 1.5 struggled to correctly answer even basic logic puzzles without external tools, the performance of Gemini 3 Deep Think and GPT-5.2 Pro suggests a qualitative shift. The fact that both models achieved this without proprietary training data or specialized mathematical fine-tuning raises profound questions about the nature of emergent reasoning in large language models.

As AI systems inch closer to human-level abstract reasoning, the implications span academia, cryptography, automated theorem proving, and even the future of scientific discovery. The First Proof challenge may now serve as a new benchmark—not just for AI capability, but for the ethical and epistemological boundaries of machine intelligence.