Google has introduced Gemini 3.1 Pro as the new “base model” for tasks where a quick response isn’t enough. The announcement, published on February 19, 2026, carries two clear messages: on one hand, a notable leap in reasoning (measured in benchmarks); on the other, a simultaneous rollout where adoption really matters: API for developers, Vertex AI for enterprises, and consumer products like the Gemini app and NotebookLM.
The company describes it as the “central intelligence” enabling recent advances in Gemini 3 Deep Think (focused on scientific, research, and engineering challenges) and now aims to transfer that improvement into everyday workflows: from synthesizing complex information to prototyping interfaces and automating tasks.
A number to headline: 77.1% in ARC-AGI-2
Google highlights a figure: 77.1% verified in ARC-AGI-2, a benchmark assessing a model’s ability to solve new logical patterns. The company states this is more than double the reasoning performance compared to Gemini 3 Pro on the same test.
In today’s context — where models are no longer competing solely to “know things” but to infer, plan, and maintain consistency — this type of metric is increasingly seen as a sign that the leap isn’t just about linguistic fluency but about the capacity to tackle unfamiliar or non-trivial problems.
Where it can be used: from lab to production (with a “preview” in between)
The deployment is broad but with an important caveat: Gemini 3.1 Pro is launching in preview. Google explains that this phase is for validating improvements and further refining areas like agent workflows before it becomes generally available.
Still, access already begins on several fronts:
- Developers (preview): Gemini API via Google AI Studio, Gemini CLI, the agent-oriented development platform Google Antigravity, and Android Studio.
- Enterprises: Vertex AI and Gemini Enterprise, the natural channels for organizations that already operate under governance, policies, and data controls.
- Consumers: the Gemini app and NotebookLM, with strategies for differentiated limits.
In the app, Gemini 3.1 Pro offers higher limits for users on Google AI Pro and Ultra plans, and in NotebookLM, access is announced as exclusive to those plans. The message is clear: Google aims to expand availability but maintains capacity control as it scales up the offering.
Less “chat,” more “doing”: what Google is choosing to teach
The announcement avoids vague promises and showcases examples that signal a change in tone: Gemini 3.1 Pro doesn’t just “answer,” it also builds.
Highlighted demonstrations include:
- Web-ready animated SVGs: generating animated graphics directly in code, providing a clear advantage for front-end: sharpness at any scale and very small file sizes compared to traditional videos.
- Synthesis of complex systems: an aerospace panel configuring a public telemetry stream to visualize the International Space Station’s orbit—an example designed to emphasize that the model can connect APIs and visualization coherently.
- Advanced interactive design: a 3D flocking simulation of starlings with interaction (hand-tracking) and generative audio that adapts to movement, aimed at prototyping rich experiences and experimentation.
This sort of case work suggests that Google is pushing 3.1 Pro into territory where models become “production tools” for technical teams: prototypes, dashboards, interfaces, and components that go from prompt to artifact without an endless chain of work.
The subtext: accelerating “agentic” workflows without multiplying tools
A recurring theme in the announcement is the concept of agentic workflows. Practically, this points to scenarios where the model does more than generate text — it must maintain context, divide a goal into steps, make decisions, and in many cases, integrate with tools.
That’s why deployment via Gemini API and Vertex AI is especially relevant for the tech sector: it’s not just about “testing a new model,” but about enabling the leap from experimentation to production in environments where governance, traceability, and system integration matter.
What developers and product teams should consider before adopting it
While being “more intelligent” doesn’t eliminate the need for thorough validation, the most reasonable approach for technical teams during a preview is:
- Select meaningful tasks (information synthesis, technical documentation, API analysis, UI prototyping, internal tool generation) and measure whether the reasoning leap results in fewer iterations.
- Assess consistency: whether the model maintains coherent decisions over long tasks and under constraints.
- Establish data safeguards: what can be entered, what is recorded, and how access is controlled, especially in enterprise scenarios.
- Compare cost/latency: advanced reasoning often involves higher consumption or response times, impacting product performance and budget.
If Gemini 3.1 Pro delivers on its promises, its competitive advantage won’t just be “speaking better” but reducing the actual cost of solving complex tasks: fewer human steps, fewer tool switches, and a more direct workflow from problem to result.
Frequently Asked Questions
What exactly improves in Gemini 3.1 Pro compared to Gemini 3 Pro?
Google highlights a significant boost in reasoning, with a 77.1% verified score in ARC-AGI-2, described as more than double Gemini 3 Pro’s performance on that benchmark.
Where can developers already integrate it?
In preview, via Gemini API (AI Studio), as well as Gemini CLI, Google Antigravity, and Android Studio.
What changes for companies already deploying AI in production?
Availability through Vertex AI and Gemini Enterprise targets adoption in environments with governance, data controls, and large-scale operations.
Why is NotebookLM relevant in this announcement?
Because Gemini 3.1 Pro is also offered in NotebookLM, oriented toward synthesis and working with information, with access announced for Pro and Ultra users.