Google Gemini 3 Explained in Depth: How Is It Different from ChatGPT GPT-5.1? A Practical Guide to Choosing for Real-World Work

1. Big Picture First – Who This Comparison Is Useful For

Google’s latest model “Gemini 3” and OpenAI’s “GPT-5.1” are both top-tier large-scale models that can be used commercially as of autumn 2025. Both companies clearly targeted reasoning, agents (autonomous task execution), and coding with this generation, and the models are shifting from “just chatbots” to “partners you actually work with.”

This article is especially for people like:

• Individuals / freelancers who want to seriously use generative AI for work
  • Creating proposals, summarizing materials, code review, transcribing video and audio, etc.
• Corporate IT / DX teams currently evaluating which model to adopt
  • Internal search / knowledge use, automated inquiry responses, workflow automation, etc.
• Product developers / startups building AI apps or SaaS
  • Want to compare API cost, context length, and agent features

We’ll first整理 the characteristics of each model, and then gently compare:

• Features (multimodal, reasoning, coding)
• Pricing and context length
• Agent capabilities and tool integration
• “Strengths and weaknesses” for real-world use

2. What Is Google Gemini 3? Organizing the Latest Features

2-1. Overview and Positioning of Gemini 3

In November 2025, Google announced “Gemini 3” as “the most intelligent model we’ve ever built.”

• The core of the model family is Gemini 3 Pro (currently in preview)
• A Gemini 3 Deep Think mode specialized for higher-accuracy reasoning will be rolled out gradually
• It’s widely integrated into Google products like the Gemini app, Search (AI Mode), Google AI Studio, and Vertex AI

Google describes it as having “world-leading multimodal understanding” and being “the most capable agentic and coding model,” strongly emphasizing reasoning, multimodal understanding, and coding agents.

2-2. Technical Specs (Gemini 3 Pro Preview)

According to the Gemini API documentation for developers, Gemini 3 Pro Preview has the following specs:

• Model ID: gemini-3-pro-preview
• Input: Text, images, video, audio, PDF (fully multimodal)
• Output: Text (image/video generation uses separate models like Imagen / Veo)
• Context length:
  • Input: Up to approx. 1,048,576 tokens (about 1 million tokens)
  • Output: Up to approx. 65,536 tokens (about 65k tokens)
• Main capabilities:
  • Function calling (tool invocation)
  • Code execution
  • File search
  • URL context (use content from URLs as context)
  • Search grounding (fact-checking via Google Search)
  • Long context, structured output, Batch API, caching, etc.

The knowledge cutoff is explicitly stated as January 2025, so in terms of data freshness it covers a very recent range.

2-3. Strengths in Benchmarks

In the official blog, Google explains that Gemini 3 Pro outperforms the previous generation (Gemini 2.5 Pro) on virtually all major benchmarks, reporting scores like:

• 1501 Elo on LMArena (chat battle arena), which is top-class at the time
• PhD-level performance on high-difficulty reasoning tasks like Humanity’s Last Exam and GPQA Diamond
• SOTA-level results on multimodal benchmarks like MMMU-Pro and Video-MMMU
• High scores on SimpleQA Verified, highlighting improved factuality

Deep Think mode further boosts reasoning performance, and particularly on metrics like ARC-AGI-2, which measure “general reasoning ability on novel problems,” high scores have been reported.

2-4. What Gemini 3 Is Good At (Use Case Examples)

Translating Gemini 3’s strengths into real-world usage might look like this:

• Understanding across huge multimodal document sets
  • Example: Throw in multiple academic paper PDFs + conference videos + experiment images and have it:
    • Structure and summarize the research background → hypotheses → experimental results → future challenges
• Creating learning content involving video, images, and audio
  • Example: Feed training videos, slides, and supplementary PDFs, then automatically generate:
    • Training manuals
    • Q&A for attendees
    • Mini quiz questions
• Using it as a coding agent
  • Google is very focused on “vibe coding” and “agentic coding,” and Gemini 3 achieves high scores on coding benchmarks like WebDev Arena and SWE-bench.
  • Example: Load an existing repository and have it propose everything from implementing new features based on specs to writing test code

Gemini 3 is also used in the Search integration (AI Mode in Search), and strengthens the experience of a “thinking search engine” by enabling interactive visualizations and simulations based on search results.

3. What Is ChatGPT GPT-5.1? The Character of the Instant / Thinking Two-Mode Setup

3-1. Overview and Positioning of GPT-5.1

In November 2025, OpenAI released “GPT-5.1” as an upgraded version of GPT-5.

• The standard generation used in ChatGPT is being switched from GPT-5 to GPT-5.1
• The model architecture has two main variants:
  • GPT-5.1 Instant
    • Everyday use: warmer, more conversational, faster responses
  • GPT-5.1 Thinking
    • For advanced reasoning: sticks with harder tasks and “thinks” more deeply
• In ChatGPT, Instant / Thinking is automatically selected based on the question

The “personality presets” (conversation style) have also been enhanced, with 8 tone options such as Default, Professional, Friendly, Casual, and Quirky.

3-2. GPT-5.1 Technical Specs (API)

Summarizing the developer-facing information, GPT-5.1 has the following characteristics:

• Model type: Multimodal (text + image input) with reasoning
• Context length:
  • Up to approx. 400,000 tokens
  • Max output of approx. 128,000 tokens
• Knowledge cutoff: September 30, 2024
• Main features:
  • Adaptive reasoning
    • For easy questions, it doesn’t overthink and responds quickly
    • For difficult questions, it uses more “thought tokens” and reasons carefully
  • Extended prompt caching
    • Can cache prompts for up to 24 hours, drastically reducing cost and latency when reusing them
  • New tools: apply_patch and shell
    • apply_patch: A tool to safely apply code diffs
    • shell: A tool to run limited shell commands
    • Both are powerful supports for agentic coding tasks

3-3. Benchmarks and Real-World Evaluation

In the official developer announcement, the following improvements over GPT-5 are reported:

• SWE-bench Verified (code-fixing tasks) improved from 72.8% → 76.3%
• Slight increases on multitask benchmarks like GPQA Diamond and MMMU
• Overall improvements on math and coding evaluations (AIME 2025, Codeforces, etc.)

On the other hand, some external reviews say it’s “more of a stability and conversational comfort upgrade than a dramatic leap,” and note that compared with Claude models and Gemini 2.5, strengths and weaknesses still vary by task.

3-4. What GPT-5.1 Is Good At (Use Case Examples)

From a practical, real-world perspective, GPT-5.1 is particularly suited for:

• Long-form, text-centric tasks
  • Example: Extract requirements from a spec document and auto-generate user stories and test cases
  • Example: Take multiple meeting notes and minutes and整理 “decisions / ToDos / risks”
• Coding with agents + tool integration
  • Combine apply_patch and shell to apply patches to real codebases and keep iterating while running tests
• Chatbots where conversational UX is critical
  • It’s easy to tune tone and personality, so it’s well suited for use cases like customer support or education where you want to deliberately design “how it talks.”

When used as part of the ChatGPT product, it also integrates naturally with other OpenAI models for voice, image generation (DALL·E), and video (Sora), which is a big plus in practice.

4. Gemini 3 vs GPT-5.1: Comparing Features, Pricing, and Usability

4-1. High-Level Comparison Table

(Based on official information and publicly available data as of November 2025)

Details on pricing, free tiers, and enterprise plans change frequently; always check the official pages for the latest before using in production.

4-2. Breaking Down Functional Pros and Cons

1) Multimodality and Long Context

• Gemini 3’s advantages
  • The combination of 1M-token context and mixed input of text, images, video, audio, and PDFs is extremely powerful.
  • It’s especially strong for tasks where you want it to “understand and reason across multiple documents and media.”
• Where GPT-5.1 stands
  • 400k tokens is more than enough for many business scenarios, and handling large codebases or knowledge bases is not really an issue.
  • It can take images as input, but for workloads that look like “video + audio + PDFs + code all at once,” Gemini 3 is architecturally a better fit.

2) Reasoning and Agent Capabilities

• Gemini 3
  • With Deep Think mode, it achieves very high scores on difficult reasoning tasks (math, AGI-style benchmarks, etc.), and when combined with environments like Google Antigravity—where multiple agents can operate IDEs and browsers—it’s approaching highly autonomous software development.
• GPT-5.1
  • Thanks to apply_patch and shell, it can run the cycle of “edit codebase with diffs + execute commands in a local environment” as one integrated loop.
  • OpenAI Atlas (browser agent) and various agent frameworks combined with external tools are also rich, and in terms of ecosystem breadth, it still has a very strong position.

3) Conversational Quality and Japanese Support

• GPT-5.1
  • It brings GPT-5—which was sometimes criticized as “cold”—back toward a warmer tone and richer personality, so you can see it as a usability-focused upgrade.
  • It emphasizes natural conversation and tone control in many languages including Japanese, making it well suited for people-facing use cases like “external-facing chatbots” or “learning support.”
• Gemini 3
  • The official blog heavily emphasizes its “leading multilingual performance,” and Japanese is definitely usable in production. However, when it comes to fine-grained “characterization” of the conversation, GPT-5.1 currently feels easier to control.

4) Pricing and Cost Optimization

• For both, flagship / Pro-class models share a common price pattern:
  • Input: around $1.25 per 1M tokens
  • Output: $10 per 1M tokens
    (as of November 2025)
• On the Gemini side, you have “Free tier + cheaper Flash / Flash-Lite,” and on the OpenAI side, you have “GPT-5 mini and GPT-5 nano” as lower-cost models, so in both ecosystems it’s easy to design a setup where only heavy workloads use flagships and lighter workloads go to cheaper models.

5. By Use Case: When Does Gemini 3 or GPT-5.1 Make You Happier?

5-1. For Individuals and Small Businesses

You’ll naturally gravitate toward Gemini 3 if you:

• Use Google Workspace (Gmail, Docs, Sheets, Slides) daily
• Want deep integration between Google services (Search, Maps, YouTube, etc.) and AI
• Want to handle content creation and analysis that includes video, audio, and images in one place

Sample scenario:

• A cooking instructor feeds lesson videos, recipe PDFs, and photos of handwritten notes into the model and has it generate:
  • Student-facing text materials
  • Practice questions
  • Lesson plans
    This workflow suits Gemini 3 very well.

You’ll naturally gravitate toward GPT-5.1 if you:

• Already have a ChatGPT Plus / Team / Enterprise subscription
• Are also considering integration with Microsoft 365 (Copilot)
• Care a lot about “how pleasant the conversation feels” and fine-grained personality tuning

Sample scenario:

• As a supplement to coaching or counseling, you set up a “soft-spoken GPT-5.1” and have it propose questions and homework tailored to each client’s situation. This is right in GPT-5.1’s wheelhouse.

5-2. From the Enterprise / Large Company Perspective

When Gemini 3 fits best

• You’re already using Google Cloud / Vertex AI and want to centrally manage data residency and governance
• You want to analyze multimodal internal data (surveillance footage, field photos, audio logs, etc.) with a single model
• You want to build advanced business apps that combine real-world geospatial information via future “Search + AI + Maps” integrations

When GPT-5.1 fits best

• You’re already building internal tools on Azure + OpenAI Service or the OpenAI API
• You want to reuse the same model family across a variety of in-house tools for coding, RPA, document generation, etc.
• You want to perform advanced automation by combining OpenAI’s browser agent (Atlas) and agent frameworks with other tools

5-3. From the Perspective of AI Developers and Startups

Reasons to choose Gemini 3

• Your product heavily depends on “analysis that spans video, images, audio, and text”
• You want to actively leverage Google’s agent development environments like Google Antigravity and the Gemini CLI
• You want natural integration with Google Search and Maps (for products with many location / map-related tasks)

Reasons to choose GPT-5.1

• You’ve already built your app on GPT-4.1 / GPT-5 and want to keep model switching cost low
• You want to deeply build out coding agents that assume apply_patch / shell
• You prioritize integration with the OpenAI ecosystem (Plugins → Actions, Atlas, various third-party tools)

In many real-world teams, a hybrid setup where you “enable both Gemini and GPT-5.1 and switch models per task” will likely be the realistic choice in terms of cost, accuracy, and risk diversification.

6. Where Things Are Heading, and How to Choose Now

6-1. Both Sides See “Agents and Coding” as the Main Battleground

If you read Google and OpenAI’s announcements side by side, it’s clear that both are focusing on:

1. Advanced reasoning
2. Agents (AIs that autonomously complete tasks using tools)
3. Coding / software development assistance

Gemini 3 uses Google Antigravity to present a world where “agents directly operate IDEs and browsers,”
while OpenAI has built apply_patch / shell into GPT-5.1, making “multi-step changes to codebases” and “running commands on local machines” standard features.

For the next 1–2 years, we can expect Google’s Gemini camp and OpenAI’s GPT-5.x camp to compete intensely on practical fronts like:

• “How safely and reliably can you build agents?”
• “How much can you boost developer productivity?”

6-2. Practical Advice for Choosing a Model Today with the Future in Mind

Finally, here’s some pragmatic advice if you’re about to choose a model now:

1. Align with the ‘center of gravity’ of your cloud and business systems
   • Already heavily invested in Google Cloud / Workspace → make Gemini 3 your mainstay
   • Already heavily invested in Azure / OpenAI API or ChatGPT-based systems → make GPT-5.1 your mainstay
2. Switch models based on the nature of the task
   • Multimodal long-document analysis → Gemini 3’s long context & multimodality
   • Text-centric tasks + UX focused on conversation → GPT-5.1 (Instant)
   • Heavy reasoning workloads → compare Gemini 3 Deep Think vs. GPT-5.1 Thinking
3. Make sure you have cost-control mechanisms
   • Since both camps’ flagships cost around “$1.25 input / $10 output per 1M tokens,”
   • Design your system from the start so lighter workloads can be offloaded to cheaper models like Flash / GPT-5 mini.
4. Avoid over-dependence on either side
   • Benchmarks and public opinion can shift every few months,
   • So build an abstract “LLM adapter layer” into your app, allowing you to switch between Gemini and GPT. This will reduce your cost of switching models in the future.

7. Summary: What’s the Best Way to Work with Gemini 3 and GPT-5.1?

• Gemini 3
  Shines in “multimodal long context + Google services integration + agentic coding,” and is especially suited to workloads where you handle video, audio, images, and text together.
• GPT-5.1
  Strikes a strong balance of “natural conversation + personality + agent APIs,” making it very easy to use for text-centric business automation and user-facing chatbots.

Rather than asking which is absolutely “better,” the answer shifts depending on:

Where your operational center lies (Google or OpenAI)
What kind of data you handle (text-centric vs. lots of video/audio)
How far you want to push agentification

If you’d like, the next step could be:

• The actual tasks you want to do (e.g., “From a webinar video, generate a report + social media summaries + quiz questions”)
• Expected number of users and requests
• Your current cloud / SaaS environment

With that information, we can整理 more detailed architectural suggestions like “for this use case, design it this way with Gemini 3” and “here, GPT-5.1 is cheaper and easier.”

References (Mostly Official Sources)

• Google: A new era of intelligence with Gemini 3
• Google AI for Developers: Gemini models (Gemini 3 Pro Preview specs)
• OpenAI: GPT-5.1: Smarter and More Conversational ChatGPT (Japanese)
• OpenAI: Introducing GPT-5.1 for developers
• The Verge: Google unveils Gemini 3
• The Verge: OpenAI says the brand-new GPT-5.1 is “warmer” and has more “personality” options