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The race for the smartest artificial intelligence is entering the next round—and this time, Google is sending the loudest signal. With Gemini 3.1 Pro, the company is leaving its competitors far behind in important performance comparisons. OpenAI and Anthropic, which have often been ahead in recent times, have been beaten in several tests.
But what is really behind this leap in performance? And what does it mean for companies, developers, and the market as a whole? The All AI portal reports on this and provides detailed figures.
A model for the tough nuts
Gemini 3.1 Pro has not simply been "improved." Google has specifically developed the system for particularly demanding tasks. The focus is on complex programming, abstract logic, and structured problem solving.
The model is designed to better organize large amounts of data, identify correlations more clearly, and translate creative ideas more directly into code. Instead of many intermediate steps, the AI should arrive at a usable result more quickly.
Google describes scenarios in which a modern user interface is created directly from the atmosphere of a novel—without cumbersome manual reworking. Whether marketing or reality: the direction is clear. It is no longer just about text, but about genuine systems thinking.
Numbers that make an impression
In a comprehensive comparison test, the "Artificial Analysis Intelligence Index v4.0," Gemini 3.1 Pro scored 57 points. This puts it ahead of Claude Opus 4.6 and Claude Sonnet 4.6. Its predecessor, Gemini 3 Pro, scored significantly lower.
The gap is also evident in specific tests. In the ARC-AGI-2 logic test, the new model scored 77.1 percent—more than twice as much as the previous version. Competitors lag noticeably behind.
In the area of academic tasks ("Humanity's Last Exam"), the system achieves 44.4 percent – without additional tools.
In competitive programming ("LiveCodeBench Pro"), the Elo rating climbs to 2887. That is a significant gap behind GPT-5.2. Google also leads the way in autonomous web searches ("BrowseComp"). Only in a specialized test for agent-based programming does a competitor model remain slightly ahead of Gemini.
Less imagination, more facts
A particularly sensitive issue with language models is their tendency to invent information. These so-called hallucinations have long been a major problem.
Google reports significant progress in this area. In a test of factual reliability, the error rate has fallen dramatically compared to the previous version. This does not mean that the model is error-free, but it is moving closer to a level that is crucial for professional applications.
For companies in particular, it is not only creativity that counts, but also reliability. AI that produces convincing nonsense is a risk in everyday business life.
More performance, stable prices
Noteworthy: Despite technical improvements, prices remain unchanged according to Google. This is likely to be a strong signal for many developers. High performance values at consistent costs further intensify competition.
Between progress and concentration of power
The surge in innovation is impressive. But it also shows how heavily the AI landscape is concentrated in the hands of a few large corporations. Those who operate the most powerful models control key tools for business, research, and administration.
Technological leadership is beneficial. However, it also creates dependency. When entire industries rely on a small number of providers, a new form of digital infrastructure emerges—privately controlled, globally effective.
The development of Gemini 3.1 Pro is undoubtedly a technical milestone. Nevertheless, the focus should not be solely on benchmark values. What is crucial is how transparently, verifiably, and responsibly these systems are used.
Technical superiority alone does not make for trustworthy technology. And that is precisely where it will become clear who is really ahead in the end.
Sources: all-ai.de
