Google’s Gemini 3.1 Pro arrived in preview with a clear message: it’s meant to be a smarter, more capable baseline for complex problem-solving, especially in workflows where an AI must plan, use tools, verify results, and iterate. Across press coverage in mid-to-late February 2026, the line claim is that Gemini 3.1 Pro “more than doubles” reasoning performance compared with Gemini 3 Pro on the ARC-AGI-2 benchmark.
That claim matters beyond bragging rights because “agentic tools” live or die by reasoning: an agent must decide what to do next, call the right tool, interpret outputs, and avoid spiraling into needless tool calls. Gemini 3.1 Pro is being positioned as an upgrade that targets exactly those failure modes, while also sparking debate from users who say the improvements come with trade-offs in tone and creativity.
What “doubling reasoning” means in practice
Multiple outlets reported concrete benchmark numbers that Google emphasized: 77.1% on ARC-AGI-2 for Gemini 3.1 Pro, compared with 31.1% for Gemini 3 Pro. That’s not just a marginal gain; it’s roughly a 2.5× jump, and it underpins the repeated “more than double” language used in coverage of Google’s announcement.
ARC-style evaluations are often cited as proxies for general reasoning because they stress pattern discovery, abstraction, and multi-step inference. For agentic systems, those skills translate into better decomposition (“what are the subproblems?”) and improved consistency when navigating long chains of actions.
Google’s positioning, as reported, frames Gemini 3.1 Pro as a stronger default model for complex problem-solving while still acknowledging that more ambitious agentic workflows are being improved during preview. In other words: the core engine is getting smarter, but the full end-to-end agent experience (planning, tool execution, verification loops) is still actively being tuned.
Gemini 3.1 Pro as a baseline for agentic tools
Agentic tools typically combine a language model with connectors to external systems, code editors, terminals, browsers, files, or proprietary enterprise apps. In that context, a “smarter baseline” is less about chat fluency and more about robustness: fewer wrong turns, less hallucinated state, and better self-checking when tool outputs contradict the plan.
Press coverage summarized Gemini 3.1 Pro’s rollout across major platforms: consumer surfaces like the Gemini app and NotebookLM, developer access via Gemini API and AI Studio, and enterprise channels through Vertex AI and Gemini Enterprise. It also appeared in environments explicitly designed for agent workflows, including Gemini CLI, Android Studio, and Google’s agentic dev environment Antigravity.
This ecosystem framing matters because agentic performance is emergent: it’s the combination of model reasoning, tool APIs, runtime guardrails, and feedback loops. A model that genuinely reasons better tends to produce measurable gains in tool efficiency, especially for “plan → act → verify → iterate” patterns that previously required heavy prompting or rigid orchestration.
Adjustable “thinking” and enterprise control
VentureBeat characterized Gemini 3.1 Pro as a “Deep Think Mini,” highlighting a three-tier thinking system (low/medium/high) that lets teams control reasoning effort. This style of control is especially relevant when deploying agentic tools at scale, where cost, latency, and reliability compete.
In many enterprises, not every request deserves maximum deliberation. A support agent that needs to retrieve policy text from a knowledge base may be fine with a lighter reasoning mode, while a workflow that reconciles conflicting financial records might warrant deeper compute and stricter verification steps.
The practical appeal is operational: a single endpoint with adjustable reasoning depth simplifies architecture. Instead of routing to multiple specialized models, teams can tune “how hard to think” per task, per user tier, or per stage of an agent pipeline (e.g., deeper for planning, lighter for summarizing tool outputs).
Tool precision and fewer calls: why GitHub Copilot cares
Gemini 3.1 Pro’s agentic angle was underscored by its appearance in GitHub Copilot’s changelog as an “agentic coding model.” The emphasis there wasn’t only on raw coding ability, it highlighted “high tool precision” and “fewer tool calls per benchmark,” particularly in edit-then-test loops.
That detail is crucial for agentic coding. Tool calls, running tests, searching code, applying edits, are where time and cost accumulate, and where mistakes can cascade. A model that needs fewer calls to converge on a correct fix is often more useful than one that writes prettier code but thrashes between actions.
GitHub noted gradual rollout and availability across VS Code, Visual Studio, github.com, and mobile. This distribution also acts as a real-world proving ground: if a model reduces tool-call over for thousands of developers, the benefit shows up quickly in latency, success rates, and user trust.
The city planner demo: multimodal agent workflows
To make “agentic” tangible, press cited a city planner-style demo used to illustrate multimodal reasoning and tool-like workflows. The example described terrain handling, infrastructure mapping, and traffic simulation, tasks that naturally involve multiple data sources and iterative planning.
City planning is a good showcase because it forces an agent to integrate constraints: geography, existing roads, projected traffic, and possibly environmental or zoning rules. A capable agent must not only generate recommendations but also justify them, update them when simulations disagree, and keep track of what has already been tried.
In agentic tool terms, this resembles a multi-step pipeline: ingest maps and constraints (multimodal input), choose actions (run a simulation, adjust a route), interpret results, and loop until a satisfactory plan emerges. Improved reasoning should reduce aimless iteration and produce more coherent, auditable decisions.
Antigravity and the broader agentic tools ecosystem
Agentic capability isn’t just a model property; it depends on the environment. Antigravity, described as an “agent-first” coding tool, is positioned around multi-agent orchestration and direct access to an editor, terminal, and browser, plus “Artifacts” like plans, screenshots, and recordings to verify work.
Those design choices map closely to what organizations want from agentic tools: traceability and verification. Artifacts turn an agent’s invisible reasoning into inspectable outputs, which helps reviewers confirm that the agent actually ran the tests it claims to have run, or that a proposed UI change matches a screenshot.
Gemini 3.1 Pro’s rollout into agent-centric surfaces (such as Antigravity and Gemini CLI) signals that Google is aiming to pair improved core reasoning with environments that make tool-use safer and more measurable. Better reasoning plus better instrumentation is often the difference between a flashy demo and a dependable workflow.
Mixed user reactions: reasoning up, “nuance” down?
Not all feedback has been uniformly positive. Tech press coverage noted a split: many users praised a leap in logical reasoning and coding benchmarks, while others claimed reduced “emotional depth, empathy, creative flexibility, and nuance.”
This tension is common when models are tuned for stronger task performance. Reinforcing step-by-step correctness, tool adherence, and concise decision-making can sometimes produce outputs that feel more rigid or less expressive, especially in open-ended writing or interpersonal conversations.
For agentic tools, the trade-off may be acceptable or even desirable: reliability often matters more than warmth. Still, product teams deploying Gemini 3.1 Pro should test both dimensions, task success and user experience, because agents that feel blunt can reduce adoption even when they’re technically correct.
Gemini 3.1 Pro’s line is straightforward: reasoning performance on ARC-AGI-2 reportedly jumped to 77.1%, versus 31.1% for Gemini 3 Pro, supporting the “more than doubles” narrative repeated across February 2026 coverage. Google’s broader message is equally clear, this is intended as a more capable baseline for complex problem-solving, with preview work continuing on more ambitious agentic workflows.
The more interesting story is how that reasoning uplift is being operationalized: adjustable thinking tiers for deployment control, tool-precision claims in agentic coding contexts like GitHub Copilot, and an expanding ecosystem of agent-first environments such as Antigravity. If the gains translate into fewer tool calls, better verification, and more stable multi-step behavior, Gemini 3.1 Pro could mark a meaningful step forward in practical agentic tools, not just benchmark charts.
