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Google Gemini 3.1 Pro Review: 77% ARC-AGI-2 Score, Benchmarks, Pricing, and What It Means for Developers in 2026

By Serenities AI · February 19, 2026 · 14 min read

Google just dropped Gemini 3.1 Pro today — and the numbers are staggering. With a 77.1% score on ARC-AGI-2, more than doubling its predecessor's 31.1%, this isn't an incremental update. It's a generational leap that puts Google firmly ahead of both Anthropic and OpenAI on key reasoning benchmarks.

If you're a developer, researcher, or AI enthusiast trying to figure out whether Gemini 3.1 Pro is worth switching to, this article breaks down every benchmark, compares it head-to-head with Claude Opus 4.6 and GPT-5.2, and helps you decide whether it's time to move.

Let's get into it.

What Is Google Gemini 3.1 Pro?

Gemini 3.1 Pro is the next iteration in Google's Gemini 3 series, building on Gemini 3 Pro which launched in November 2025. It's Google's most advanced model for complex tasks, and it arrives as a natively multimodal system capable of processing text, audio, images, video, and entire code repositories in a single context.

The headline specs:

• 1 million token context window — process entire codebases, lengthy documents, or hours of video in one pass
• 64,000 token output — generate complete applications, comprehensive reports, or detailed analyses without truncation
• Natively multimodal — text, audio, images, video, and code are all first-class inputs
• Released February 19, 2026 — available today in preview

This isn't a minor version bump. The jump from Gemini 3 Pro to 3.1 Pro represents one of the largest single-generation improvements we've seen in reasoning ability from any lab.

Where to Access Gemini 3.1 Pro

Google is making Gemini 3.1 Pro widely available across its ecosystem. Here's where you can start using it today:

• Gemini API — direct API access for developers
• Google AI Studio — browser-based prototyping and testing
• Gemini CLI — command-line access for terminal-native workflows
• Google Antigravity — Google's development platform
• Android Studio — integrated AI assistance for Android developers
• Vertex AI — enterprise-grade deployment on Google Cloud
• Gemini Enterprise — for organizational deployments
• Gemini app — consumer-facing chat interface
• NotebookLM — AI-powered research and note-taking (Pro and Ultra users only)

Free users can try Gemini 3.1 Pro directly in the Gemini app. If you're on AI Pro or Ultra paid tiers, you get higher rate limits and priority access. NotebookLM integration is exclusive to Pro and Ultra subscribers.

The breadth of availability is notable. Google is clearly betting on widespread adoption across developer tools, enterprise platforms, and consumer products simultaneously.

The Headline Number: 77.1% on ARC-AGI-2

Let's talk about the number that matters most.

ARC-AGI-2 is widely considered one of the most meaningful benchmarks for measuring genuine reasoning ability. Unlike memorization-heavy benchmarks, ARC-AGI tests a model's ability to identify abstract patterns and apply them to novel situations — the kind of fluid intelligence that separates genuine understanding from sophisticated pattern matching.

To put 77.1% in context:

• Claude Opus 4.6 (Anthropic's flagship) scores 68.8% — Gemini 3.1 Pro leads by 8.3 points
• GPT-5.2 (OpenAI's latest) scores 52.9% — Gemini 3.1 Pro leads by 24.2 points
• Claude Sonnet 4.6 scores 58.3% — Gemini 3.1 Pro leads by 18.8 points

This is the largest lead any model has held on ARC-AGI-2 across competitors. Google hasn't just caught up on reasoning — they've pulled ahead decisively.

Gemini 3.1 Pro vs Competitors: Full Benchmark Breakdown

One benchmark doesn't tell the full story. Let's look at the complete picture across 15 benchmarks from the official model card.

Benchmark	Gemini 3.1 Pro	Gemini 3 Pro	Sonnet 4.6	Opus 4.6	GPT-5.2
Humanity's Last Exam (no tools)	44.4%	37.5%	33.2%	40.0%	34.5%
HLE (Search+Code)	51.4%	45.8%	49.0%	53.1%	45.5%
ARC-AGI-2 (verified)	77.1%	31.1%	58.3%	68.8%	52.9%
GPQA Diamond	94.3%	91.9%	89.9%	91.3%	92.4%
Terminal-Bench 2.0	68.5%	56.9%	59.1%	65.4%	54.0%
SWE-Bench Verified	80.6%	76.2%	79.6%	80.8%	80.0%
SWE-Bench Pro	54.2%	43.3%	—	—	55.6%
LiveCodeBench Pro (Elo)	2887	2439	—	—	2393
SciCode	59%	56%	47%	52%	52%
APEX-Agents	33.5%	18.4%	—	29.8%	23.0%
τ2-bench Retail	90.8%	85.3%	91.7%	91.9%	82.0%
MCP Atlas	69.2%	54.1%	61.3%	59.5%	60.6%
BrowseComp	85.9%	59.2%	74.7%	84.0%	65.8%
MMMU-Pro	80.5%	81.0%	74.5%	73.9%	79.5%
MMMLU	92.6%	91.8%	89.3%	91.1%	89.6%

That's a lot of numbers. Let's break down what they actually mean.

Where Gemini 3.1 Pro Dominates

Gemini 3.1 Pro takes the top spot on the majority of benchmarks tested. Here are its strongest showings:

• ARC-AGI-2 (77.1%) — The headline result. Leads Opus 4.6 by 8.3 points and GPT-5.2 by 24.2 points. This is the single largest gap between frontier models on any major reasoning benchmark right now.
• BrowseComp (85.9%) — Web browsing and comprehension. Nearly 2 points ahead of Opus 4.6 (84.0%) and 20 points ahead of GPT-5.2 (65.8%). If you're building agentic tools that browse the web, this matters enormously.
• MCP Atlas (69.2%) — Model Context Protocol evaluation. Gemini 3.1 Pro leads all competitors here — nearly 10 points above Opus 4.6 (59.5%). This is critical for developers building tool-using AI agents.
• APEX-Agents (33.5%) — Agentic task completion. Leads Opus 4.6 by 3.7 points and GPT-5.2 by 10.5 points. Google has clearly optimized for agentic workflows.
• Terminal-Bench 2.0 (68.5%) — Command-line task execution. Leads Opus 4.6 by 3.1 points and GPT-5.2 by 14.5 points.
• LiveCodeBench Pro (2887 Elo) — Competitive programming. An Elo of 2887 puts it nearly 500 points above GPT-5.2's 2393 and 448 points above Gemini 3 Pro's 2439.
• GPQA Diamond (94.3%) — Graduate-level science questions. The highest score among all models tested.
• Humanity's Last Exam, no tools (44.4%) — Pure reasoning without tool use. Beats Opus 4.6 (40.0%) by 4.4 points.

Where Competitors Still Lead

Gemini 3.1 Pro doesn't win everywhere. Here's where the competition holds on:

• HLE with Search+Code — Claude Opus 4.6 leads at 53.1% vs Gemini's 51.4%. When tool use is involved, Opus still has a slight edge on this particular exam benchmark.
• SWE-Bench Verified — Opus 4.6 leads at 80.8% vs Gemini's 80.6%. The gap is razor-thin (0.2 points), but Anthropic's flagship still technically leads on this widely-watched software engineering benchmark.
• SWE-Bench Pro — GPT-5.2 leads at 55.6% vs Gemini's 54.2%. Another narrow gap, but OpenAI holds the edge here.
• τ2-bench Retail — Both Opus 4.6 (91.9%) and Sonnet 4.6 (91.7%) beat Gemini's 90.8% on this retail task automation benchmark.

The pattern is clear: Gemini 3.1 Pro dominates on reasoning, browsing, and agentic benchmarks, while the competition holds narrow leads on specific software engineering and tool-use tasks.

Gemini 3.1 Pro vs Gemini 3 Pro: What Changed?

The improvement from Gemini 3 Pro (November 2025) to Gemini 3.1 Pro is remarkable across the board. Let's look at the most dramatic jumps:

Benchmark	3 Pro	3.1 Pro	Improvement
ARC-AGI-2	31.1%	77.1%	+46.0 pts (+148%)
BrowseComp	59.2%	85.9%	+26.7 pts
MCP Atlas	54.1%	69.2%	+15.1 pts
APEX-Agents	18.4%	33.5%	+15.1 pts (+82%)
Terminal-Bench 2.0	56.9%	68.5%	+11.6 pts
SWE-Bench Pro	43.3%	54.2%	+10.9 pts
LiveCodeBench Pro	2439	2887	+448 Elo

The ARC-AGI-2 jump is historic. Going from 31.1% to 77.1% in roughly three months suggests Google made fundamental architectural or training improvements to the model's abstract reasoning capabilities — not just incremental scaling.

The improvements in BrowseComp (+26.7 pts) and APEX-Agents (+15.1 pts) also signal that Google heavily invested in agentic capabilities. This model was clearly designed to not just answer questions, but to act on them — browsing the web, using tools, and executing multi-step workflows.

What Can Gemini 3.1 Pro Actually Do?

Benchmarks tell you about capability ceilings. But what does this model look like in practice?

Google's demos showcased several impressive capabilities that highlight both the model's reasoning depth and its multimodal nature:

Creative Coding and Visualization

Gemini 3.1 Pro can generate animated SVGs from text descriptions. This isn't just static code generation — it's the model understanding visual concepts, animation timing, and producing working interactive graphics from natural language prompts.

Real-Time Data Applications

Google demonstrated a live ISS (International Space Station) dashboard built with Gemini 3.1 Pro. The model handled real-time data integration, visualization, and live updates — showcasing its ability to build functional, production-grade applications that interact with external data sources.

Advanced 3D and Interactive Experiences

Perhaps the most visually striking demo was a 3D starling murmuration simulation with hand tracking. The model generated code for a complex particle simulation that responds to hand movements in real time, demonstrating both its coding ability and its understanding of physics-based animation.

Complex Reasoning and Data Synthesis

With a 1M token context window, Gemini 3.1 Pro can ingest entire codebases, research papers, or lengthy documents and synthesize insights across them. The 64K token output means it can produce comprehensive reports, complete applications, or detailed analyses without hitting output limits that plague shorter-context models.

The Competitive Landscape: Code Red at OpenAI?

According to reporting from Mashable, Google's Gemini 3 Pro launch in November 2025 allegedly triggered a "code red" at OpenAI. If that's true, Gemini 3.1 Pro is likely to intensify the pressure.

Here's how the competitive picture looks after today's release:

Gemini 3.1 Pro vs Claude Opus 4.6

Gemini 3.1 Pro beats Opus 4.6 on the majority of benchmarks tested:

• ✅ HLE no tools (44.4% vs 40.0%)
• ✅ ARC-AGI-2 (77.1% vs 68.8%)
• ✅ GPQA Diamond (94.3% vs 91.3%)
• ✅ Terminal-Bench 2.0 (68.5% vs 65.4%)
• ✅ SciCode (59% vs 52%)
• ✅ APEX-Agents (33.5% vs 29.8%)
• ✅ MCP Atlas (69.2% vs 59.5%)
• ✅ BrowseComp (85.9% vs 84.0%)
• ✅ MMMU-Pro (80.5% vs 73.9%)
• ✅ MMMLU (92.6% vs 91.1%)
• ❌ HLE with tools (51.4% vs 53.1%)
• ❌ SWE-Bench Verified (80.6% vs 80.8%)
• ❌ τ2-bench Retail (90.8% vs 91.9%)

The verdict: Gemini 3.1 Pro leads on 10 out of 13 comparable benchmarks against Opus 4.6. Opus holds narrow advantages on tool-augmented tasks and SWE-Bench Verified, but the reasoning and agentic gaps favor Google significantly.

Gemini 3.1 Pro vs GPT-5.2

Against OpenAI's GPT-5.2, Gemini 3.1 Pro's dominance is even more pronounced. It leads on nearly every benchmark tested, with GPT-5.2 only holding the edge on SWE-Bench Pro (55.6% vs 54.2%). The 24-point gap on ARC-AGI-2 and 20-point gap on BrowseComp are particularly striking.

What This Means for Agentic AI

Three benchmarks in particular signal where the industry is heading: APEX-Agents, MCP Atlas, and BrowseComp.

Gemini 3.1 Pro leads all three — and by significant margins. This matters because 2026 is rapidly becoming the year of AI agents. Models aren't just answering questions anymore. They're browsing the web, executing code, using tools via protocols like MCP, and completing multi-step workflows autonomously.

Gemini 3.1 Pro's dominance on agentic benchmarks suggests Google has specifically optimized for this use case. The 33.5% score on APEX-Agents (vs Opus 4.6's 29.8% and GPT-5.2's 23.0%) means it's better at completing complex, multi-step agentic tasks. The 69.2% on MCP Atlas means it's better at using external tools. The 85.9% on BrowseComp means it's better at understanding and navigating the web.

For developers building AI agents — whether for customer support, research automation, code generation, or data analysis — these results matter. Platforms like Serenities AI are already integrating Gemini models into their workflows, and the 3.1 Pro upgrade could significantly improve agent reliability and task completion rates.

Who Should Upgrade to Gemini 3.1 Pro?

Not every user needs the most powerful model. Here's a practical breakdown of who benefits most from Gemini 3.1 Pro:

Definitely Upgrade If You...

• Build AI agents — The APEX-Agents, MCP Atlas, and BrowseComp scores make this the best model for autonomous tool-using agents right now.
• Need strong reasoning — The ARC-AGI-2 score of 77.1% is unmatched. If your use case involves abstract reasoning, pattern recognition, or novel problem-solving, this is the best option available.
• Work with large codebases — 1M token context + 64K output + strong SWE-Bench and LiveCodeBench scores make this excellent for code understanding and generation at scale.
• Do multimodal work — Native support for text, audio, images, video, and code repos means you can process diverse inputs without switching models or building complex pipelines.
• Need competitive programming-level code — An Elo of 2887 on LiveCodeBench Pro is the highest we've seen. For algorithmic problem-solving, this is the best model available.
• Use Google's ecosystem — If you're already on Vertex AI, Android Studio, or Google Cloud, the integration is seamless.

Consider Staying with Opus 4.6 If You...

• Rely heavily on tool-augmented reasoning — Opus 4.6 still leads on HLE with Search+Code (53.1% vs 51.4%). If your workflow depends on the model using external tools to reason, the difference is small but real.
• Focus primarily on SWE-Bench-style tasks — Opus 4.6 leads by 0.2 points on SWE-Bench Verified. If your primary use case is exactly this kind of software engineering task, the models are essentially tied.
• Are deeply invested in Anthropic's ecosystem — If your tooling, prompts, and workflows are optimized for Claude, the switching cost may outweigh the benchmark advantages.

Consider GPT-5.2 If You...

• Need SWE-Bench Pro performance — GPT-5.2 leads with 55.6% vs Gemini's 54.2%. A narrow edge, but it exists.
• Are locked into OpenAI's ecosystem — If your infrastructure is built on OpenAI's APIs, switching has costs beyond model performance.

Free Users

If you're not paying for any AI service, the fact that Gemini 3.1 Pro is available for free in the Gemini app is a significant advantage. You can test the most powerful reasoning model available without spending a dollar. AI Pro and Ultra tiers give you higher limits for production use.

The Bigger Picture: Google's AI Momentum

Step back from the individual benchmarks and a larger story emerges. Google went from playing catch-up in the AI race to leading on multiple fronts in roughly a year.

Gemini 3 Pro (November 2025) was already competitive. Gemini 3.1 Pro doesn't just iterate — it leapfrogs. The 148% improvement on ARC-AGI-2 in just three months suggests Google's research team has found something — whether it's a training methodology, architectural innovation, or data approach — that produces outsized gains in reasoning ability.

The breadth of availability is also notable. By launching simultaneously across the Gemini API, AI Studio, Gemini CLI, Antigravity, Android Studio, Vertex AI, Gemini Enterprise, the Gemini app, and NotebookLM, Google is making sure every developer segment — from hobbyists to enterprise teams — has access on day one.

This is a different Google than the one that fumbled the Bard launch. This is a company that's executing with focus and speed.

What to Expect Next

Gemini 3.1 Pro is launching in preview today, which means Google will likely refine the model based on user feedback before a full general availability release. Historically, preview periods for Gemini models have lasted a few weeks to a couple of months.

The areas to watch:

• Real-world performance — Benchmarks and production use don't always correlate perfectly. The community will stress-test this model heavily over the coming weeks.
• Pricing — Google hasn't announced pricing changes with this release. Keep an eye on the Vertex AI pricing page for updates as the model moves from preview to GA.
• Competitive responses — If Gemini 3 Pro triggered a "code red" at OpenAI, what does 3.1 Pro trigger? Expect Anthropic and OpenAI to accelerate their release timelines.
• Agentic frameworks — With top scores on APEX-Agents and MCP Atlas, expect a wave of new agentic tools and frameworks built specifically for Gemini 3.1 Pro.

Frequently Asked Questions

Bottom Line

Gemini 3.1 Pro is Google's strongest model release to date — and arguably the strongest single model release from any lab in 2026 so far. The 77.1% ARC-AGI-2 score is the headline, but the consistent dominance across reasoning, agentic, browsing, and coding benchmarks tells a broader story: Google has built a model that excels where it matters most for the future of AI.

Is it perfect? No. Opus 4.6 still edges it out on a few specific tasks, and the real-world community feedback will matter more than any benchmark table. But if you're choosing a model today for complex reasoning, building AI agents, or processing large multimodal inputs, Gemini 3.1 Pro just became the default recommendation.

The AI race just got faster. And right now, Google is leading.