The marketing wars between OpenAI and Google have reached peak intensity. Benchmark claims fly back and forth. Both sides declare victory on different metrics. For enterprise leaders trying to make actual purchasing decisions, it’s exhausting.

I’ve spent the past two months testing both platforms across real business use cases with clients. Here’s what I found.

The Test Setup

We ran both Google Gemini (1.5 Pro) and GPT-4 (specifically GPT-4 Turbo) through a battery of enterprise-relevant tests:

• Document summarisation (contracts, reports, meeting notes)
• Data analysis from structured sources
• Email and content drafting
• Code generation and review
• Customer service response generation
• Knowledge base Q&A

Each test used the same prompts and source materials. We measured accuracy, response time, cost, and subjective quality ratings from business users.

Where GPT-4 Won

Complex reasoning tasks: For multi-step analysis requiring inference and judgement, GPT-4 consistently produced more coherent outputs. A contract review task that required identifying interconnected risk clauses showed the biggest gap – GPT-4 caught dependencies that Gemini missed.

Code generation: Both produced working code, but GPT-4’s code was cleaner, better commented, and required fewer iterations to get right. For a client building internal tools, this translated to real time savings.

Consistency: GPT-4’s outputs were more predictable. Same prompt, same input, similar output. Gemini showed more variation, which sometimes meant pleasant surprises but often meant unpredictable quality.

Where Gemini Won

Long document processing: Gemini’s larger context window (up to 1 million tokens in the latest version) was a genuine advantage for analysing lengthy documents. We tested with a 200-page tender response – Gemini handled it natively while GPT-4 required chunking.

Speed: Gemini was consistently faster, sometimes by 40-50%. For interactive use cases where users are waiting, this matters.

Multimodal tasks: When working with documents that mixed text, tables, and images, Gemini was more reliable at extracting information correctly. A facilities management client processing inspection reports (lots of photos with annotations) saw notably better results.

Price: At current pricing, Gemini is about 30% cheaper per token for comparable models. At scale, this adds up.

Where Both Were Roughly Equal

General email drafting: Both produced professional, appropriate business correspondence. Users couldn’t consistently identify which AI had written which email.

Meeting summary generation: Accuracy and usefulness were comparable when summarising call transcripts.

Simple Q&A: For straightforward questions against a knowledge base, both performed well enough that differences were negligible.

The Integration Question

Technical capability is only part of the decision. Integration matters enormously.

If you’re a Microsoft shop: The choice is complicated. Microsoft’s OpenAI partnership means GPT-4 powers Copilot and Azure OpenAI Service. Native integration with your existing stack is a significant advantage. But Google Workspace customers might find Gemini’s integration more natural.

If you’re a Google Workspace shop: Gemini integrates natively. That said, the enterprise Gemini features in Workspace are still maturing. Don’t expect the same depth as Microsoft’s Copilot offering today.

If you’re multi-cloud: Both offer good API access. The decision comes down to your specific use case requirements.

The Pricing Reality

Let’s talk numbers (as of September 2024):

GPT-4 Turbo (via Azure OpenAI):

• Input: $0.01 per 1,000 tokens
• Output: $0.03 per 1,000 tokens

Gemini 1.5 Pro:

• Input: $0.00125 per 1,000 characters (roughly equivalent to $0.005 per 1,000 tokens)
• Output: $0.00375 per 1,000 characters (roughly equivalent to $0.015 per 1,000 tokens)

For a heavy enterprise user processing millions of tokens monthly, the difference is material. We modelled one client’s expected usage and found Gemini would cost approximately $15,000/month versus $22,000/month for GPT-4.

Whether that savings justifies any capability trade-offs depends on your specific use cases.

My Recommendation Framework

Choose GPT-4 if:

• Complex reasoning and analysis is your primary use case
• You’re heavily invested in the Microsoft ecosystem
• Consistency and predictability are critical (regulated industries)
• You’re doing significant code generation

Choose Gemini if:

• You regularly work with very long documents
• Speed of response impacts user experience
• Cost is a significant constraint
• You have multimodal processing needs
• You’re a Google Workspace organisation

Consider both if:

• Different use cases have different requirements
• You want negotiating leverage with vendors
• You’re still evaluating and not ready to commit

The Honest Assessment

Neither platform is dramatically better than the other for most business use cases. The benchmarks showing 5% improvements on specific tests don’t translate to meaningful real-world differences for typical enterprise applications.

What matters more:

• How well does it integrate with your existing systems?
• What’s the total cost of ownership including implementation?
• How comfortable is your team with the platform?
• What’s the vendor’s enterprise support like?

The AI itself is becoming commoditised. The wrapper around it – the integration, the security, the support – is where differentiation actually happens.

Final Thought

In six months, this comparison will be outdated. Both platforms are evolving rapidly. GPT-5 and Gemini 2 are on the horizon. Any decision you make today should include flexibility to switch if the landscape changes.

Don’t overcommit. Build abstraction layers in your architecture. Keep your options open.

The best AI strategy isn’t picking a winner. It’s building systems that can adapt as the technology evolves.