A comprehensive survey of over 2,700 AI builders across 55 countries has found that while OpenAI’s ChatGPT remains marginally more popular than Anthropic’s Claude, the real dividing line is experience. Beginners overwhelmingly reach for ChatGPT, but as developers gain years of hands-on practice, they shift toward Claude — with engineers and enterprise teams reporting a clear preference for Anthropic’s assistant.
Conducted by GenAI Fund and reported by TNGlobal on July 17, 2026, the study surveyed 2,719 approved AI builders who actively use generative AI tools. The results paint a picture of a maturing market where tool choice reveals as much about the user as the technology.
The Overall Numbers Look Nearly Identical
At first glance, the race is too close to call. ChatGPT was used by 77.9% of respondents, nudging past Claude at 76.6%. Google’s Gemini came in third at 61.1%. These figures aren’t market share in the traditional sense—81.7% of builders reported using more than one platform, meaning the percentages reflect overlap, not exclusive loyalty.
That overlap is the first practical takeaway: the typical AI builder already juggles multiple assistants. For development teams and IT admins, the question isn’t “which one?” but “which ones, for which tasks?”
The Experience Cliff: When ChatGPT’s Lead Evaporates
GenAI Fund segmented respondents by years of AI-tool experience, and the trend is unmistakable. Among newcomers with less than a year under their belt, ChatGPT leads Claude by a comfortable margin: 79.3% to 70.9%. In the 1–2 year bracket, Claude catches up at 79.2% versus ChatGPT’s 78.2%. From there, the gap widens in Claude’s favor. For those with six to ten years of experience, Claude pulls ahead 78.4% to 72.4%. At the extreme end—more than a decade of experience—both settle at 73.8%.
| Experience Level | ChatGPT Usage | Claude Usage |
|---|---|---|
| Less than 1 year | 79.3% | 70.9% |
| 1–2 years | 78.2% | 79.2% |
| 3–5 years | Tied (approx) | Tied (approx) |
| 6–10 years | 72.4% | 78.4% |
| 10+ years | 73.8% | 73.8% |
Source: GenAI Fund survey, as reported by TNGlobal.
The shift is more than a curiosity. It suggests that as developers accumulate real-world, production-grade experience, they gravitate toward Claude’s style of output—often cited as more cautious, context-aware, and code-reliable. Early in the learning curve, ChatGPT’s freewheeling fluency may feel more accessible, but the steady migration toward Claude among veterans implies a difference in substance, not just taste.
Job Role: Students Love ChatGPT, Engineers Back Claude
The role-based data echoes the experience story. Among students, 84.1% use ChatGPT versus 76.4% for Claude. Engineers flip the script: 84.4% use Claude, while only 79.2% use ChatGPT. Enterprise teams show a similar tilt, with Claude at 77.7% to ChatGPT’s 74.2%.
These gaps are modest in absolute terms, but they point to distinct ecosystems. Students may value ChatGPT’s broad conversational ability for learning and quick prototyping, while engineers and enterprise teams demand fine-grained control, safer code generation, and integration into complex CI/CD pipelines—areas where Claude has built a reputation for discipline.
The Multi-Model Reality: Gemini, DeepSeek, and the Rest
Beyond the top two, the survey confirms that no single tool owns the developer desktop. Gemini’s 61.1% makes it a solid third, and further down the list, niche players are carving their own spaces: DeepSeek at 18.2%, Hugging Face at 12.8%, Qwen at 11.6%, and LangChain at 9.8%. Notably, 27.5% of respondents reported using at least one Chinese AI model, reflecting the global spread of the technology.
For those tracking Microsoft’s AI footprint, Azure OpenAI was cited by only 5.2% of respondents as a platform used directly. That number, however, is almost certainly an undercount of enterprise deployment. Many organizations access OpenAI models through Azure’s managed APIs even while developers may interact with ChatGPT directly for testing or ad‑hoc tasks. The survey asked about individual builder usage, not cloud spending or production workload distribution.
What This Means for Windows Developers and IT Teams
The survey’s sample is heavily technical: 72.5% use Python, 43.6% use JavaScript or TypeScript, and 17.5% use Java. So while the findings don’t represent casual Windows PC owners, they carry weight for anyone building or managing AI toolchains on Windows.
For Windows‑first development shops, the implications are threefold:
- Don’t standardize on a single assistant. With over 80% already using multiple models, your team likely needs access to at least ChatGPT and Claude, plus Gemini for certain workflows.
- Match the tool to the task and the skill level. Junior devs may ramp up faster with ChatGPT’s intuitive interface, while senior engineers should be equipped with Claude for critical code generation and review. If your team uses GitHub Copilot (which now supports multiple models), consider the underlying model choice as a productivity lever.
- Keep an eye on Azure OpenAI. For organizations deeply integrated into the Microsoft ecosystem, Azure OpenAI provides enterprise-grade compliance, private networking, and fine‑tuning—benefits that individual builder surveys won’t capture. The 5.2% figure shouldn’t dissuade you from evaluating it as a secure gateway to the same GPT family that powers ChatGPT.
How to Decide: Choosing the Right AI Assistant for Your Work
If you’re a developer trying to build your stack, or an IT manager provisioning tools, use these practical steps:
- Start with a task audit. List the types of prompts you send most often: code generation, refactoring, debugging, documentation, learning new concepts, or architecting systems. Different models excel at different tasks.
- Test both across your core scenarios. Give ChatGPT and Claude the same five real problems your team faced last sprint. Compare not just correctness but also consistency, safety, and integration ease.
- Consider the context window and memory. For long, multi‑file coding sessions, Claude’s large context windows (up to 200k tokens in some configurations) can reduce iterative prompting.
- Watch for enterprise features. If you need data isolation, audit logs, or usage controls, look beyond the free chat interfaces to API‑level offerings such as Claude Enterprise or Azure OpenAI.
- Plan for churn and portability. Models and APIs evolve fast. Encapsulate AI calls behind a thin abstraction layer so you can swap backends without rewriting your tooling.
- Don’t ignore the open‑weight options. With DeepSeek and Qwen gaining traction, and Hugging Face at nearly 13%, the ability to self‑host or fine‑tune open models can be a cost‑effective complement to commercial APIs.
Outlook: The Fork in the AI Road
The GenAI Fund survey captures a snapshot of an ecosystem in transition. ChatGPT’s beginner dominance may shrink as Claude’s developer‑focused refinements draw more early‑career adopters, or ChatGPT could close the seniority gap with upcoming features. What’s certain is that the multi‑model habit is now entrenched.
For Windows users and IT leaders, the survey’s message is clear: invest in flexibility, and build AI‑assisted workflows that assume diversity, not monoculture. The assistant that helps a new intern fix a JavaScript bug may not be the same one that architects your next microservice — and that’s exactly how it should be.
This article is based on a GenAI Fund survey originally reported by TNGlobal on July 17, 2026. All figures reflect self‑reported usage by 2,719 approved AI builders across 55 countries.