Google Unveils Major Upgrade to Gemini 3 Deep Think for Scientific and Engineering Rigor

Google announced on February 12, 2026, the deployment of a significant enhancement to its specialized reasoning model, Gemini 3 Deep Think. This iteration is engineered to address complex, real-world challenges in scientific research and engineering, particularly those characterized by incomplete data or ambiguous parameters. The upgrade signals a strategic focus by Google toward specialized, rigorous AI analysis, developed in collaboration with numerous scientists and researchers to blend deep scientific comprehension with practical engineering utility.

The enhanced Deep Think version is immediately accessible to users subscribed to Google AI Ultra within the Gemini application. Furthermore, Google has initiated an early access program, making the model available to select external researchers, engineers, and enterprises via the Gemini API for the first time. This expansion suggests an intent to integrate these deep reasoning capabilities directly into critical industrial and high-level research workflows, positioning the tool as potential infrastructure for specialized computation.

The model’s enhanced capabilities were validated across a suite of frontier AI benchmarks. Gemini 3 Deep Think achieved gold-medal level results on the written sections of the 2025 International Math Olympiad, as well as the 2025 International Physics Olympiad and Chemistry Olympiad. On the ARC-AGI-2 benchmark, which tests adaptability and abstract reasoning, the model secured a verified score of 84.6%, a result confirmed by the ARC Prize Foundation. Additionally, it achieved a score of 93.8% on the graduate-level science benchmark, GPQA Diamond, and a 50.5% score on the CMT-Benchmark in theoretical physics.

Real-world validation from academic institutions underscores the model's utility in substantive research. Lisa Carbone, a mathematician at Rutgers University, utilized Deep Think to scrutinize a highly technical mathematics paper, where the model successfully pinpointed a subtle logical error that had previously evaded human peer review. Concurrently, researchers within the Wang Lab at Duke University, including Haozhe “Harry” Wang, leveraged the system to optimize crystal growth methodologies for semiconductor materials, successfully designing a recipe for thin films exceeding 100 micrometers in precision.

A key practical capability highlighted is the model's ability to interpret a hand-drawn sketch, model the resulting physical system through code, and generate a corresponding 3D-printable file, facilitating rapid prototyping. This release aligns with an industry shift where enterprises are transitioning reasoning models from experimental phases into stable production architectures, moving away from a generalized approach toward specialized reasoning engines.