Gemini 3.1 Pro vs GPT-5.2 vs Claude Opus 4.6: Which AI Model Wins in 2026?

Google just dropped Gemini 3.1 Pro (preview) — and the numbers are hard to ignore. In Google DeepMind's published benchmark table, Gemini 3.1 Pro posts a 77.1% on ARC-AGI-2, a dramatic jump over Gemini 3 Pro's 31.1% — not a minor version bump, but a real step change in reported reasoning performance.

But benchmarks aren't everything. OpenAI's GPT-5.2 and Anthropic's Claude Opus 4.6 aren't going anywhere, and each still has areas where they clearly lead. So which model actually wins in February 2026?

I dug into the data — verified scores, real pricing, and the caveats Google would rather you not think about. Here's what I found.

What Is Gemini 3.1 Pro?

Here's what changed:

• ARC-AGI-2 jumped from 31.1% (Gemini 3 Pro) to 77.1% (Gemini 3.1 Pro) — a ~2.5x increase in reported abstract reasoning
• Agentic capability improved: APEX-Agents rose from 18.4% to 33.5%
• Coding & terminal tasks: SWE-Bench Verified = 80.6% (near the top tier) and Terminal-Bench 2.0 = 68.5% (leading among the compared frontier models in the same table)
• Availability (preview): Gemini API (AI Studio), Vertex AI, Gemini CLI, Gemini App, NotebookLM, and Antigravity

That's the marketing pitch. Let's see if the numbers hold up in a head-to-head.

Benchmark Showdown: Gemini 3.1 Pro vs GPT-5.2 vs Claude Opus 4.6

Here's the full comparison across key benchmarks. All scores are from official announcements or verified third-party reports as of February 19, 2026.

The Headline Number: ARC-AGI-2

Let's talk about the elephant in the room. A 77.1% on ARC-AGI-2 is genuinely significant. For context:

• GPT-5.2 (high compute): 52.9%
• Claude Opus 4.6: 68.8%
• Gemini 3 Pro: 31.1%

The r/singularity community is buzzing about this today, and rightfully so. ARC-AGI-2 is designed to test novel reasoning — problems the model hasn't seen before. A ~24-point gap over GPT-5.2 is not marginal.

That said, let's keep it honest: these are Google's self-reported numbers. Independent verification takes time. Some Reddit skeptics are already raising the "fine-tuned for the test" concern, which is worth flagging even if it's speculative at this point. We'll update this article as third-party evaluations come in.

Where Each Model Actually Wins

Benchmarks give you a scorecard. Real-world use cases give you an answer. Here's where each model has genuine advantages.

Gemini 3.1 Pro: Best for Reasoning, Agentic Tasks, and Coding

If you need a model that can think through novel problems, Gemini 3.1 Pro is the new leader. The ARC-AGI-2 and APEX-Agents scores suggest a meaningful leap in the model's ability to:

• Tackle multi-step reasoning chains
• Autonomously complete complex agentic workflows
• Handle real-world software engineering tasks (80.6% SWE-Bench Verified is no joke)
• Browse and synthesize information from the web (BrowseComp 85.9%)

Claude Opus 4.6: Best for Expert-Level Knowledge and Nuanced Analysis

Claude Opus 4.6 isn't far behind — and in some areas, it's still ahead:

• Humanity's Last Exam (with tools): 53.1% vs Gemini's 51.4% — when the questions are truly at the frontier of human knowledge, Claude still edges out
• Claude's Sonnet 4.6 Thinking variant leads in GDPval-AA Elo (1633 vs 1317), suggesting stronger performance in certain evaluation and alignment domains
• Anthropic's focus on safety and instruction-following means Opus 4.6 tends to be more reliable for sensitive, high-stakes outputs where you need consistent quality

GPT-5.2: Best for Ecosystem, Multimodal, and Cost Efficiency

OpenAI's GPT-5.2 may trail on several benchmarks, but it has structural advantages:

• Pricing at $1.75/$14.00 per M tokens makes it the most cost-effective frontier model (source: openai.com/api/pricing)
• The OpenAI ecosystem (ChatGPT, API, Codex variants) remains the most widely integrated across third-party tools
• GPT-5.3-Codex leads SWE-Bench Pro (Public) at 56.8%, showing OpenAI's specialized coding variants remain competitive
• Broadest multimodal capabilities with mature vision, audio, and tool-use features

Pricing Comparison

Cost matters. Here's what each model costs at the API level:

The Bigger Picture: What Gemini 3.1 Pro Means for the AI Race

Let's zoom out.

The Reasoning Gap Is Closing — Fast

Six months ago, the narrative was "OpenAI leads reasoning, Anthropic leads safety, Google leads multimodal." That's no longer clean. Gemini 3.1 Pro's ARC-AGI-2 score (77.1%) doesn't just close the reasoning gap — it blows past the competition by a significant margin.

This matters because ARC-AGI-2 isn't a knowledge test. It measures the ability to reason about patterns the model has never seen. A 25-point lead over GPT-5.2 on this specific benchmark suggests Google has made a genuine architectural or training breakthrough, not just scaled up data.

Agentic AI Is the New Battleground

The APEX-Agents score (33.5%) is arguably more important than the headline ARC-AGI-2 number. It shows Gemini 3.1 Pro can autonomously complete complex, multi-step tasks nearly twice as well as Gemini 3 Pro (18.4%) and significantly better than GPT-5.2 (23.0%) and Opus 4.6 (29.8%).

This is where the real money is. Agentic AI — models that can browse the web, write code, execute tasks, and chain together complex workflows — is the killer app of 2026. Google's investment in this direction (see: Google Antigravity, Gemini CLI) signals they're serious about owning this space.

But Benchmarks Aren't the Whole Story

A few important caveats:

1. Self-reported scores. Google published these benchmarks. Independent verification is pending. The AI community has learned to be cautious about launch-day numbers.
2. Benchmark ≠ vibes. Models that score well on standardized tests don't always feel better in practice. Real-world usability, instruction following, creativity, and consistency matter — and these are harder to measure.
3. Rivals aren't standing still. OpenAI has GPT-5.3-Codex already showing improvements on SWE-Bench Pro. Anthropic's Claude roadmap likely has responses in the pipeline. Today's leader is tomorrow's baseline.
4. The "fine-tuned for the test" question. Some in the r/singularity community are questioning whether the ARC-AGI-2 score reflects genuine reasoning improvement or optimization for the benchmark format. It's a fair question that only time and independent evaluation will answer.

The Bottom Line

Gemini 3.1 Pro is the most impressive single-model release of 2026 so far. Topping 13 of 16 benchmarks isn't luck — it's a clear signal that Google DeepMind is firing on all cylinders. But "most impressive launch" and "best model for your use case" aren't the same thing.

• Raw reasoning and agentic power? → Gemini 3.1 Pro
• Expert knowledge and safety? → Claude Opus 4.6
• Cost efficiency and ecosystem? → GPT-5.2

The AI arms race just got another chapter. And honestly? We're all better for it.

Frequently Asked Questions

Is Gemini 3.1 Pro better than GPT-5.2?

On many published benchmarks, yes — including ARC-AGI-2 (77.1% vs 52.9%), GPQA Diamond (94.3% vs 92.4%), and APEX-Agents (33.5% vs 23.0%). However, GPT-5.2 remains more cost-effective on input ($1.75/M) and has broader third-party integration support.

How much does Gemini 3.1 Pro cost?

Gemini 3.1 Pro (preview) is priced at $2/$12 per 1M tokens (≤200K context) and $4/$18 (>200K) on the Gemini API pricing page. Preview pricing/terms may change — verify the current rate card before production rollout.

What is Gemini 3.1 Pro's ARC-AGI-2 score?

Gemini 3.1 Pro scored 77.1% on ARC-AGI-2 — up from 31.1% for Gemini 3 Pro (about 2.5x higher). In the same published table, Claude Opus 4.6 is 68.8% and GPT-5.2 is 52.9%.

Where does Claude Opus 4.6 still beat Gemini 3.1 Pro?

Claude Opus 4.6 leads on Humanity's Last Exam (with tools) at 53.1% vs Gemini's 51.4%, and the Claude Sonnet 4.6 Thinking variant leads GDPval-AA Elo (1633 vs 1317). For expert-level knowledge tasks and safety-critical applications, Claude remains highly competitive.

Is Gemini 3.1 Pro available now?

Which AI model is best for coding in 2026?

Gemini 3.1 Pro is one of the strongest all-around coding models: SWE-Bench Verified = 80.6% (very close to the top) and Terminal-Bench 2.0 = 68.5% (leading in the published comparison table). That said, Claude Opus 4.6 slightly edges SWE-Bench Verified at 80.8%, and specialized variants (e.g., GPT-5.3-Codex) may lead on certain coding-only leaderboards.

Should I switch from GPT-5.2 to Gemini 3.1 Pro?