Beyond ChatGPT? What Google’s Gemini Can (and Can’t) Do Right Now

Can Google’s AI really do things ChatGPT can’t? That was the headline-grabbing claim from DeepMind CEO Demis Hassabis, following a wave of Gemini updates and demos that highlighted real-time, multimodal capabilities. If you’re a founder, exec, or curious professional trying to choose the right AI for work, what should you make of it?

In this guide, we break down what was claimed, what’s actually available, and how Gemini and ChatGPT compare for real-world use. We also add helpful context from recent announcements and credible sources so you can make an informed call.

What DeepMind claimed — and why it matters

“DeepMind’s AI chatbot can do things that ChatGPT cannot.” — coverage of comments by Demis Hassabis, CEO of Google DeepMind.

The core of the claim centers on Gemini’s multimodality and long-context understanding, along with live, agent‑like assistance demonstrated at Google I/O 2024. In particular, Google showcased:

• Long context windows in Gemini 1.5 (up to 1 million tokens generally available; 2 million in preview), enabling analysis of long documents, videos, and codebases in a single prompt.
• Real-time multimodal assistants (Project Astra), which can see and talk about what your camera sees, respond quickly, and remember context across interactions.

These are undeniably impressive. But the competitive picture is dynamic: OpenAI has also launched real-time, multimodal models (GPT‑4o), while differences in context window size, availability, and ecosystem support can tip the balance depending on your use case.

Gemini’s standout edge: long-context, multimodal understanding

One area where Gemini 1.5 currently distinguishes itself is context length. With up to 1 million tokens generally available (and 2 million tokens in private preview), Gemini can ingest and reason over far larger inputs than most mainstream models today. In practice, that means you can upload and analyze:

• Hundreds of pages of contracts or research in one go
• Hours of transcribed meetings or training videos
• Entire code repositories or large software design docs

According to Google, Gemini 1.5 improves on “long-context retrieval” in tests designed to check whether the model can find specific details deeply buried in massive inputs. While vendor-reported results should always be taken with healthy skepticism, the Gemini 1.5 technical overview and subsequent updates indicate consistent progress here.

By contrast, OpenAI’s GPT‑4o currently offers a shorter context window (commonly cited at 128k tokens) with strong multimodal performance, but not at the million-token scale. For workloads that genuinely require ingesting very large, mixed-format inputs (documents, audio, images, and code) in a single shot, Gemini’s long context can be a real advantage.

Real-time assistants: Project Astra vs. GPT‑4o

Google’s Project Astra demoed an assistant that sees through your camera, understands context quickly, and responds naturally in speech. It’s fast, cohesive, and feels closer to a continuously aware AI helper than a simple chatbot.

OpenAI’s GPT‑4o also enables real-time, multimodal interactions (text, image, and audio), with low latency and an expressive voice mode. Both companies are racing to deliver “AI you can talk to” that perceives and reasons across modalities.

Key differences to consider:

• Availability: Google is weaving Gemini into Android, Search, and Workspace, with Project Astra capabilities rolling out in stages. OpenAI is rolling advanced voice and multimodal features to ChatGPT and API over time. Check what’s live in your region and plan for staged adoption.
• Latency and stability: Demos are fast; real-world performance varies by network, device, and model tier. For mission-critical use, test with your own content and workflows.
• Privacy and data controls: If you’re pointing a camera at whiteboards or screens, review enterprise data policies, retention, and admin controls before deployment.

Reasoning, agents, and tool use

Both ecosystems now support “agentic” behaviors: using tools, calling APIs, reading files, and taking multi-step actions.

• Google: Gemini integrates with Workspace (Gmail, Docs, Sheets) and Android, with enterprise controls via Google Cloud. Google’s focus includes long-context reasoning across PDFs, videos, and Drive content, and agent-like workflows in Workspace.
• OpenAI: ChatGPT supports a rich marketplace of custom GPTs via the GPT Store and tool use via the Chat Completions and Assistants APIs. Many third-party apps, no-code tools, and developer platforms already integrate tightly with OpenAI models.

For now, neither vendor has “solved” reasoning. Models can chain steps and call tools, but may still invent facts or mis-handle edge cases. This is why high-stakes workflows should include verification steps and guardrails.

Where ChatGPT still leads

Despite Gemini’s momentum, ChatGPT offers strengths that may matter more for some teams:

• Ecosystem and distribution: The GPT Store has a large catalog of specialized GPTs and community-built skills, making it easy to pilot niche use cases quickly.
• Developer familiarity: Many startups already built around OpenAI APIs, which can simplify integration, hiring, and vendor management.
• Mature third-party tooling: From analytics to prompt ops and RAG infrastructure, there’s a robust ecosystem around OpenAI models.

Pricing and availability snapshot

Consumer and team options evolve fast, but as of publication:

• Gemini Advanced is available via Google One AI Premium (which also bundles extra storage and features). Enterprise access is available through Google Cloud and Workspace add-ons.
• ChatGPT offers free and Plus plans for individuals, with Team and Enterprise options for organizations, plus API access for developers.

For serious adoption, confirm the exact model tiers you’ll get (e.g., Gemini 1.5 Pro vs. Flash; GPT‑4o vs. GPT‑4o mini), rate limits, context windows, and data policies.

How to choose for your business

Use this quick checklist to make a pragmatic choice:

• Input size: Do you need to process very large files or long videos in one pass? If yes, Gemini’s long context can be a differentiator.
• Real-time interaction: If you’re building voice-forward or camera-aware assistants, test both GPT‑4o and Gemini/Astra variants and compare latency and quality.
• Ecosystem fit: If your team lives in Google Workspace and Android, Gemini may integrate more smoothly. If you rely on GPTs, the GPT Store, and existing OpenAI workflows, ChatGPT may be faster to operationalize.
• Governance: Confirm enterprise controls, data residency, audit logs, and retention — especially for regulated industries.
• Total cost: Model choice affects cost per request, context window costs, storage, and human-in-the-loop verification time.

Limits and risks to keep in mind

• Hallucinations still happen: Both vendors warn that models can produce plausible but incorrect answers. Use citations, retrieval, and review loops for critical decisions.
• Latency can spike: Real-time models depend on network and inference load. Build fallbacks and timeouts into user-facing flows.
• Evaluation is hard: Benchmarks don’t always reflect your data. Run task-specific tests and measure accuracy, throughput, and user satisfaction.

Bottom line

Yes, Gemini can do some things ChatGPT currently can’t — notably, handling million-token, multimodal inputs and certain real-time camera-and-voice experiences showcased by Project Astra. But ChatGPT’s ecosystem, distribution, and developer momentum remain compelling.

The best choice depends on your workload. If you need long-context analysis across varied media, start with Gemini. If you want speed to market via existing tools and community skills, ChatGPT is hard to beat. For many teams, the pragmatic answer is to pilot both and standardize on the model that delivers the best accuracy, latency, and cost for your highest-value tasks.

FAQs

Is Gemini really better than ChatGPT?

It depends. Gemini 1.5 offers much longer context and strong multimodal features, which can be a game-changer for large, mixed-format inputs. ChatGPT (GPT‑4o) excels in real-time multimodal too and benefits from a vast ecosystem. Test both on your own data.

What’s special about Gemini’s long context?

With up to 1M tokens (and 2M in preview), Gemini can analyze very large documents, videos, and codebases in one request. That reduces fragmentation and can improve reasoning across related materials.

Can ChatGPT do real-time voice and vision?

Yes. GPT‑4o supports real-time text, image, and audio interactions with low latency. Availability and performance vary by plan and rollout stage.

Which is more secure for enterprise use?

Both offer enterprise offerings with admin controls and data protections. Security depends on your configuration, usage policies, and vendor agreements. Review each provider’s enterprise documentation and compliance posture.

Should startups pick one model or use many?

Start with the model that best fits your primary use case, but design a thin abstraction so you can switch or mix models as pricing, features, and performance evolve.

Sources

1. The Independent coverage of Demis Hassabis’s claim
2. Google: Gemini 1.5 — long-context, multimodal updates
3. Google DeepMind: Project Astra overview
4. OpenAI: Introducing GPT‑4o (real-time multimodal)
5. OpenAI: The GPT Store announcement
6. Google One AI Premium (Gemini Advanced)
7. OpenAI: ChatGPT plans and availability
8. Anthropic: Claude 3 family (context and reasoning reference)