Gemini 3.1 Pro: What Just Dropped and Why It Actually Matters
Google just shipped Gemini 3.1 Pro — the latest in what's becoming a near-monthly cadence of frontier model releases. Here's our breakdown: real benchmarks, 2026 SOTA comparisons, and an honest take on whether chasing the top of the leaderboard still makes sense.
Models are shipping faster than most teams can evaluate them. A new frontier release every few weeks means benchmarks are yesterday's news before you've finished reading the announcement. What actually matters — especially if you're building with AI or deciding where to invest — is what these models are actually good at, what they cost, and whether they change what's possible for your use case.
This is the first post in our Model Watch series. Every time a meaningful frontier model drops, we'll give you our take: capabilities, real benchmarks, cost, and a plain-language verdict. We're starting with Gemini 3.1 Pro, which Google shipped this week.
What Is Gemini 3.1 Pro?
Gemini 3.1 Pro is Google DeepMind's latest flagship, released February 18, 2026. It builds directly on Gemini 3 Pro (November 2025) and is the current top of Google's Pro line for complex reasoning and multimodal work.
The "3.1" is a meaningful step, not a marketing bump. Google describes it as the upgraded reasoning engine behind the Gemini 3 Deep Think update — twice the verified performance of 3 Pro on ARC-AGI-2, more reliable on agentic tasks, and better at following complex multi-step instructions.
Available now in the Gemini app, AI Studio, Vertex AI, NotebookLM, and via API.
The GTA Labs Take
These are very good, yet unsurprising numbers — ever since Google got back to the top with Gemini 2.5, the improvement curve has been predictable. The demos I've seen are genuinely impressive. But something's shifted.
It used to be simple: whatever model led the benchmarks was your default for everything. That's starting to change. It now feels like each model has its strengths for different use cases, and I'd believe anyone who argues for their preferred model if it's working for them. It's starting to come down to taste — think of it like music, scotch, or food. Some are more expensive, but that doesn't always mean better. The fit for your specific use case is starting to matter more than any number on a chart.
Personally: since I mainly do agentic coding, Opus and Sonnet still lead for most things. But I jump back to Codex when I get stuck on a hard problem, and Gemini when I need something specific on the backend. Replacing my furnace blower motor? Not without Gemini. Got a list of ingredients and need a healthy dinner for the family? GPT just feels right — I can't fully explain it. The point is: there are some clear winners for particular tasks, but at the end of the day you need to test your own use cases and see what fits.
Next up in this series: a breakdown of my favourite models for each use case.
— Greg Mousseau
Where the Numbers Actually Land
We're using the Artificial Analysis Intelligence Index v4.0 — the most comprehensive independent evaluation available. It averages ten benchmarks across reasoning, coding, agentic work, knowledge, and hallucination resistance. The old standbys (MMLU, GPQA) are too saturated to differentiate frontier models in 2026.
What the Intelligence Index measures (10 evaluations):
- GDPval-AA — real professional work across 44 occupations, blind pairwise ELO
- Terminal-Bench Hard — agentic terminal/coding tasks
- HLE (Humanity's Last Exam) — 2,500 of the hardest multi-domain questions
- GPQA Diamond — graduate-level scientific reasoning
- SciCode — scientific coding
- CritPt — research-level physics reasoning
- AA-Omniscience — knowledge + hallucination penalization
- IFBench — instruction following
- AA-LCR — long-context retrieval
- τ²-Bench Telecom — domain-specific agentic reasoning
| Model | Intelligence Index | HLE | GPQA | GDPval-AA (ELO) | Terminal-Bench | Hallucination Rate |
|---|---|---|---|---|---|---|
| Gemini 3.1 Pro | 57 | 44.4% | 68.8% | 1,316 | ~65% | 50% |
| Claude Opus 4.6 | 53 | 40.0% | — | 1,606 | 74.7% | — |
| Claude Sonnet 4.6 | 51 | — | — | 1,633 | — | — |
| GPT-5.2 (xhigh) | 50 | 34.5% | — | 1,580 | 64.9% | — |
| GLM-5 | 46 | — | — | 1,350 | — | — |
| Kimi K2.5 Thinking | 44 | — | — | — | 43.2% | — |
| Gemini 3 Pro | 43 | ~35% | — | 1,210 | 64.7% | 88% |
| DeepSeek V3.2 | 42 | ~31% | — | — | — | — |
| Qwen 3.5 (235B) | ~40 | ~28% | — | — | — | — |
Intelligence Index: Artificial Analysis v4.0 composite score (10 benchmarks). Higher = better across all columns except hallucination rate. — = not yet independently verified at time of writing.
What the table actually tells you:
Gemini 3.1 Pro leads the overall Intelligence Index by 4 points and dominates on raw reasoning (HLE, GPQA, CritPt), knowledge accuracy, and hallucination reduction — dropping from 88% to 50% hallucination rate vs its predecessor. Claude leads for agentic work (GDPval-AA, Terminal-Bench) — if your use case is autonomous coding or multi-step task execution, Opus 4.6 and Sonnet 4.6 still win. GPT-5.2 is solid but no longer top of the pack on any single benchmark we track. The Chinese open-weight models (GLM-5, Kimi K2.5) are competitive and running the full benchmark suite at roughly half the cost of the frontier closed models — worth watching for teams that want to self-host.
The Use-Case Breakdown
| Category | Rating | Notes |
|---|---|---|
| 🧠 Reasoning & Math | ★★★★★ | 77.1% on ARC-AGI-2 — best current score. 44.4% on HLE — top of the leaderboard. |
| 💻 Coding | ★★★★☆ | Excellent for complex, multi-file agentic work. Not necessarily faster than Sonnet or Codex for simple tasks. |
| 🖼️ Multimodal | ★★★★★ | Text, images, video, audio, PDFs all in one context. Best-in-class for mixing modalities in a single prompt. |
| 📄 Long Context | ★★★★★ | 1M token window. Entire codebases, full document libraries, a year of Slack history — all fair game. |
| 🤖 Agentic / Tool Use | ★★★★☆ | Built for agentic workflows. Streaming function calls and multimodal function responses are new. |
| 💰 Cost Efficiency | ★★★☆☆ | Capable model at a premium. Right tool if you need the power; overkill for simple queries. |
Pricing
No premium for the upgrade over Gemini 3 Pro:
| Context | Input | Output |
|---|---|---|
| Under 200K tokens | $2.00 / M | $12.00 / M |
| Over 200K tokens | $4.00 / M | $18.00 / M |
Free tier via AI Studio (rate-limited). For enterprise/production: Vertex AI, AI Ultra, or Gemini Enterprise.
The Demo That Made Everyone Pay Attention
Two demos from the launch are worth understanding — not because of what they look like, but because of what they imply:
1. Live ISS Orbit Dashboard — Gemini 3.1 Pro was prompted to build a real-time aerospace dashboard in a single session: wiring a live telemetry stream, visualizing the ISS's current orbit, generating the full interface. That's API reasoning, streaming data, and UI design — combined, in one context window.
2. Interactive 3D + Hand Tracking + Generative Audio — From a natural language prompt, the model generated a 3D interactive scene with gesture controls and custom audio, entirely in one session. No scaffolding, no hand-holding.
The pattern: Gemini 3.1 Pro shines when you need to hold large amounts of heterogeneous context (code, data, instructions, assets) and produce something that spans multiple technical layers at once.
The GTA Labs Demo: CN Tower 360°
We wanted to build something similar — but with a Toronto twist.
The prompt we gave Gemini 3.1 Pro:
"Build an interactive 3D experience that simulates looking out from the CN Tower's rotating restaurant at 351m. Include Toronto's skyline, Lake Ontario, Rogers Centre, the Financial District, and the Scotiabank Arena. The view should auto-rotate like the real restaurant (one revolution every 72 minutes, sped up for demo). Allow the user to drag to look around and scroll to zoom. Sunset lighting. Label the key landmarks. Make it beautiful."
One session. No iteration. Here's the result:
The CN Tower's rotating restaurant completes one full revolution every 72 minutes. We sped it up for the demo — drag to look around, scroll to zoom. Open full screen →
From Prompt to Production
That first prompt worked beautifully — Gemini generated a complete Three.js scene with labeled buildings, sunset lighting, auto-rotation, and interactive controls. But before we could share it here, we had to make a few edits:
- The view was too wide. Looking straight out from the restaurant, you could see past Niagara Falls — which meant loading millions of tiles during a full rotation. We restricted the max view angle to 65° to keep tile loading manageable.
- API costs wouldn't scale. Every tile loaded directly through Google's Map Tiles API. One user doing a full rotation was thousands of API calls. We built a caching proxy on GCP (Cloud Function + GCS bucket) so tiles are fetched once and served from cache for every subsequent visitor.
- The CN Tower was blocking its own view. The original camera height of 553m is the tower's height above ground — but Toronto sits ~80m above sea level. The camera was clipping through the tower's 3D model during rotation. Bumping to 633m (553 + 80) put us cleanly above the antenna tip.
- Most users won't interact. We captured a video of the full rotation as the default experience — the live 3D only loads when someone actively clicks "Explore Live," which means zero API cost for casual readers.
The takeaway: even though the average user can generate this in a single prompt, it still takes a developer to make it shareable. The gap between "works on my screen" and "works for thousands of visitors" is where the real engineering happens — and for now, that gap still exists.
Can you do this via the API? Yes — entirely. You don't need the desktop app or Vertex for this class of demo. AI Studio is the fastest way to prototype. The API is what makes it shippable.
What about the live CN Tower webcam? The cntower.ca/live-views webcams are real and streaming. Integrating them directly would require a server-side CORS proxy (browser security blocks direct embed). The 3D simulation above doesn't need it — but it's a natural next step.
Should You Use It?
Yes, if:
- You're building an agentic workflow over large, complex inputs
- You have a multimodal problem (video + text, image + code, audio + documents)
- You need frontier reasoning — algorithm design, scientific synthesis, hard math
- You want to experiment with complex interactive generation
Not yet, if:
- You need production stability (still in preview — GA is coming)
- Your use case is conversational AI or simple content generation
- Cost per token matters more than capability headroom
What's Next
This is the first post in our Model Watch series. We'll cover every major frontier release: what changed, who it's for, and what you can actually build with it.
Next up: A breakdown of our go-to models for specific use cases — agentic coding, front-end, back-end generation, research synthesis, document processing, and a few things that might surprise you.
If you want to know which model fits your next project, reach out. That's exactly the kind of question we help answer.
Tech Stack
Gemini 3.1 Pro CesiumJS Google 3D Tiles Cloud Functions GCS Google AI Studio