An honest builder's review — no benchmarks gaming, just production reality
The Context: We Run Both
Let me be clear about my bias upfront: TKC Group runs primarily on Google Cloud and Vertex AI. Gemini 3 Pro is our default reasoning model. But we also use GPT-5 for specific use cases, and we've benchmarked both extensively in production.
This isn't a benchmark paper. This is what it's actually like to build production AI products with both models, every day, across 12 companies.
Head-to-Head: Production Metrics
| Metric | Gemini 3 Pro | GPT-5 |
|---|---|---|
| Median Latency (reasoning) | 2.1s | 3.4s |
| Cost per 1M input tokens | $1.25 | $2.50 |
| Cost per 1M output tokens | $5.00 | $10.00 |
| Context Window | 1M tokens | 128K tokens |
| Code Generation Quality | 9/10 | 9/10 |
| Long-Form Reasoning | 9.5/10 | 9/10 |
| Creative Writing | 7/10 | 8.5/10 |
| Structured Output (JSON) | 9.5/10 | 8/10 |
| Image Understanding | 9/10 | 8.5/10 |
| Native Image Generation | ✅ (Gemini 3 Pro Image) | ✅ (DALL-E 4) |
Where Gemini 3 Pro Wins
**1. Cost.** It's half the price. When you're processing thousands of marketing analyses per day across 12 companies, that 50% cost difference is the margin between profitable and not.
**2. Latency.** Gemini 3 Pro consistently returns 35-40% faster for reasoning tasks. For our real-time agent interactions (Slack, web chat), this is the difference between 'snappy' and 'awkward pause.'
**3. Context window.** 1M tokens vs 128K tokens isn't even close. For our marketing data analysis, where we need to process months of campaign data in a single call, Gemini's window is essential.
**4. Structured output.** Gemini 3 Pro is better at returning clean JSON. Less hallucination in structured formats, fewer malformed objects. This matters when your entire pipeline depends on parseable output.
**5. Vertex AI integration.** If you're already on GCP (we are), the integration is seamless. IAM-based auth, native Firestore connections, Cloud Run deployment — no API key management.
Where GPT-5 Wins
**1. Creative writing.** For marketing copy, social media posts, and anything that needs to feel 'human,' GPT-5 still has an edge. It's more natural, more varied, and less likely to fall into formulaic patterns.
**2. Instruction following on ambiguous prompts.** When the prompt is vague or conversational, GPT-5 is better at inferring intent. Gemini 3 Pro is more literal.
**3. Third-party ecosystem.** The OpenAI ecosystem is massive. More tutorials, more libraries, more examples. If you're prototyping fast and want to copy-paste from Stack Overflow, GPT-5 is easier to get started with.
**4. Plugins and function calling.** OpenAI's function calling is slightly more mature and predictable. Gemini's is catching up but still has occasional quirks with nested tool calls.
The Verdict
If I had to pick one? **Gemini 3 Pro**, and it's not close for our use case.
The cost advantage alone would be enough. But combined with the speed, context window, and GCP-native integration, it's the right choice for anyone building production AI systems at scale.
GPT-5 is a better writing partner. Gemini 3 Pro is a better engineering partner. Know what you're building and choose accordingly.
The real winner? The builder who uses both and routes to the right model for each task. That's what Kane does — Gemini 3 Pro for analysis, Flash for classification, and GPT-5 when creative voice matters.
Q: Should I switch from GPT-5 to Gemini?
A: If cost and speed matter to you (they should), yes — for reasoning and structured tasks. Keep GPT-5 for creative work. The best architecture uses both via a routing layer.
Q: What about Claude?
A: We tested Claude 4 Opus. Excellent at long-form analysis and code review, but the API is less mature for production agentic use cases. It's a strong model in a weaker ecosystem. If Anthropic improves their tooling, it becomes a serious third option.