What Is Gemini 3.1 Pro? Google's Most Capable AI Model Explained (2026)

Hey fellow model-switchers — if you've been burning tabs comparing frontier AI benchmarks lately, you're in the right place. I've been deep in eval sheets for the past few days, and when Gemini 3.1 Pro dropped February 19, my first reaction wasn't "cool announcement." It was: does this actually change the stack I'm running, or is it just marketing math?

That's the question I'm tracking here. Not the keynote version — the version that answers whether you should update your model ID today or wait and see.

I'm Hanks. I test AI tools inside real workflows. Not demos. Real work.

What Is Gemini 3.1 Pro?

Gemini 3.1 Pro is Google DeepMind's newest flagship model, released February 19, 2026. It builds directly on Gemini 3 Pro and is positioned as the most advanced Pro-tier model Google has shipped — designed for complex reasoning, agentic workflows, and multimodal tasks that require more than a surface-level response.

The short version: same pricing as Gemini 3 Pro, meaningfully stronger reasoning, new thinking controls.

How It Fits in Google's Model Lineup

Google runs three tiers in the Gemini 3 family right now:

• Gemini 3 Flash — fast, cheap, good for high-volume simple tasks
• Gemini 3.1 Pro — the new general-purpose workhorse for complex reasoning and production workloads
• Gemini 3 Deep Think — the research-grade heavy hitter, designed for science and engineering problems

3.1 Pro sits squarely in the middle: more capable than Flash, more accessible and affordable than Deep Think. If you're building AI pipelines or doing serious knowledge work, this is the tier to pay attention to.

Why ".1" Instead of a Full New Version

This is a first for Google. Previous mid-cycle updates were labeled ".5" (Gemini 2.5 Pro launched March 2025). The ".1" instead signals a shift toward more frequent incremental releases, as 9to5Google noted in their February 19 coverage.

What makes it a ".1" and not a ".5"? Scope. 3.1 Pro is a targeted core intelligence upgrade — specifically the reasoning engine — without a complete architectural overhaul. The 1M token context window stays. The multimodal inputs stay. What changed is how the model thinks through problems.

What Actually Changed vs Gemini 3 Pro

Here's where things get genuinely interesting. I was skeptical a ".1" bump would matter much. Then I looked at the numbers.

Reasoning Leap — ARC-AGI-2 from 31.1% to 77.1%

ARC-AGI-2 tests whether a model can solve novel abstract reasoning patterns — problems it hasn't seen in training. It's one of the harder benchmarks to game with memorization. Gemini 3 Pro scored 31.1%. Gemini 3.1 Pro hit 77.1%.

That's not incremental. That's a different class of performance.

Here's the full benchmark comparison across the current frontier, sourced from Google's official model card and VentureBeat's first impressions coverage (February 2026):

The ARC-AGI-2 gap is the headline number. But GPQA Diamond at 94.3% — PhD-level science questions — is what matters most if you're running research or technical document workflows.

Coding and Multimodal Upgrades

JetBrains' Director of AI described 3.1 Pro as delivering "up to 15% improvement over the best Gemini 3 Pro Preview runs" — faster, more efficient, requiring fewer output tokens for more reliable results.

Two other practical upgrades worth tracking:

SVG animation generation. 3.1 Pro can generate website-ready animated SVGs directly from text prompts. Sounds niche — until you're prototyping UI components and want to skip the back-and-forth with a design tool.

Expanded output capacity. Max output is now 65,536 tokens, resolving a documented limitation where Gemini 3 Pro frequently truncated code generation around 21,000 tokens. For developers generating full modules or refactoring large files, this is a real fix. One gotcha: the default maxOutputTokens is only 8,192. You have to set it explicitly to unlock the full 64K.

New Feature: Three Thinking Levels (Low / Medium / High)

This is the architectural change I kept coming back to. Gemini 3 Pro had two thinking modes: low and high. 3.1 Pro adds a medium tier — and critically, redefines what "high" means.

Here's the full mapping, per the official Gemini API developer guide:

The practical implication: instead of routing different task types to different models, you can use a single model endpoint and dial the reasoning depth per request. Routine summarization runs on Low. Hard multi-step problems get High — which VentureBeat describes as behaving like a "mini version of Gemini Deep Think."

Migration note: if you were previously using Gemini 3 Pro on High, switch to Medium in 3.1 Pro first. The quality level is roughly equivalent, and you won't blow your token budget by accident.

Key Specs in Plain English

1M Token Context Window — What That Lets You Do

The 1M context window carried over from Gemini 3 Pro. What's new is that the stronger reasoning engine can use that context more effectively.

In practice, 1M tokens means you can feed the model in a single prompt:

• An entire mid-size codebase
• Several hours of audio transcripts
• Up to 900 individual images
• Up to 1 hour of video (without audio)

For developers doing codebase-level analysis, this changes the workflow. You're not chunking files anymore. You load the whole project and ask questions about it. The Vertex AI documentation confirms the full specs: 1,048,576 input tokens, 65,536 output tokens, multimodal input across text, audio, images, video, and PDF.

Where You Can Access It Right Now

3.1 Pro launched in preview February 19, 2026. Model ID: gemini-3.1-pro-preview.

Pricing — same as Gemini 3 Pro, per the Google AI Studio pricing page:

• $2.00 per 1M input tokens (under 200K tokens)
• $12.00 per 1M output tokens (under 200K tokens)
• $4.00 / $18.00 per 1M tokens for prompts over 200K

Who Should Actually Use It?

Best Fit: Developers, Researchers, Enterprise Teams

3.1 Pro makes the most sense for workflows where reasoning quality directly affects output quality — not just speed or volume.

Developers get the 1M context window for full-codebase analysis, 80.6% SWE-Bench Verified for real engineering tasks, and three-tier thinking to control cost per request. Here's a working Python example using the Gemini API:

python

from google import genai
from google.genai import types
client = genai.Client()
# Medium thinking — recommended default for most production tasks
response = client.models.generate_content(
    model="gemini-3.1-pro-preview",
    contents="Review this pull request for bugs and logic errors...",
    config=types.GenerateContentConfig(
        thinking_config=types.ThinkingConfig(thinking_level="MEDIUM"),
        max_output_tokens=65536
    )
)
# High thinking — for complex multi-file debugging
response_deep = client.models.generate_content(
    model="gemini-3.1-pro-preview",
    contents="Find the root cause of this intermittent race condition...",
    config=types.GenerateContentConfig(
        thinking_config=types.ThinkingConfig(thinking_level="HIGH"),
        max_output_tokens=65536
    )
)

Note: don't mix thinking_level and the legacy thinking_budget in the same request — that returns a 400 error.

Researchers and analysts benefit most from the GPQA Diamond score (94.3%) and expanded output limit. If you're synthesizing large document sets or working with scientific literature, the reasoning improvement over 3 Pro is real.

Enterprise teams get a single model endpoint that handles a range of task complexities. Databricks' CTO described it as achieving "best-in-class results on OfficeQA" — their benchmark for grounded reasoning across tabular and unstructured data.

Where Claude or GPT Still Has an Edge

Honest take: 3.1 Pro doesn't win everything.

Claude Opus 4.6 still leads on expert task preferences (GDPval-AA Elo: 1606 vs 3.1 Pro's 1317) and computer use benchmarks. If your workflow involves heavy document editing or nuanced instruction-following in office-style tasks, Anthropic's models hold an edge there.

GPT-5.3-Codex leads on specialized coding benchmarks — specifically Terminal-Bench 2.0 and SWE-Bench Pro. If competitive coding or highly specialized software engineering is your primary use case, it's worth evaluating in parallel.

3.1 Pro's advantage is breadth at a favorable price point. Strongest general-purpose model available right now for the cost. That's meaningful — it just doesn't mean it's automatically right for every specific task.

Is It Worth Switching Today?

Here's what I'd actually do: update the model ID, set thinking_level to Medium as your default, and run it on whatever tasks you're currently using Gemini 3 Pro for. The API format is identical, the price is identical, and the reasoning quality is genuinely better. There's no reason not to make that swap.

The harder call is whether 3.1 Pro should replace Claude or GPT in your stack. That depends on your task mix. Run it against what you're actually shipping — not just the ARC-AGI-2 headline.

At Macaron, we built our agent to turn your AI conversations into structured, executable plans — one sentence to create the workflow, no app-switching, no context lost between steps. Try it free at macaron.im and run it through a real task yourself.

Frequently Asked Questions

Is Gemini 3.1 Pro a full new version or just an update? It's an update — the first ".1" increment Google has shipped. The core architecture carries over from Gemini 3 Pro. What changed is the reasoning engine and the addition of the Medium thinking tier.

Is Gemini 3.1 Pro available free? The Gemini app (Pro mode) gives consumer access. Developers get a free tier for prototyping in Google AI Studio. Paid API access starts at $2.00 per 1M input tokens — the same price as Gemini 3 Pro.

How do I switch from Gemini 3 Pro? Update your model ID from gemini-3-pro-preview to gemini-3.1-pro-preview. The API format is identical. If you were using High thinking mode, start with Medium on 3.1 Pro — the quality is roughly equivalent to 3 Pro's High, at lower cost.

What's the difference between thinking levels in 3.1 Pro? Low is fast and cheap — best for simple tasks. Medium is the recommended default for most production use, roughly equivalent to 3 Pro's High mode. High activates Deep Think Mini, which delivers near-Deep Think reasoning depth at higher cost and latency.

Can Gemini 3.1 Pro replace my entire model stack? Probably not entirely. It's the strongest general-purpose option right now, but Claude still edges it on expert task preferences and computer use. Test it against your actual task distribution before committing.