Why I’m Switching from ChatGPT to Claude for Coding

My name is Jeffrey Mdala, and I am an AI Engineer & Founder based in Lusaka, Zambia. I run Zambian Online Education Company (ZOEC), where I built eskulu, an AI-powered e-learning platform for the Zambian ECZ curriculum, and Zedpastpapers, which now serves more than 200,000 users every month. Because I spend a lot of time building software, testing AI tools, and thinking about how these systems can help African businesses and students, I pay close attention to which models actually perform best in real work.

Right now, my recommendation is simple: if you are using ChatGPT mainly for coding, it may be time to switch to Claude.

I do not say that lightly. I have tested these tools for myself, and based on model performance, benchmarks, context size, and reasoning ability, Claude is currently ahead for coding tasks.

Why this matters to me as a builder

I am not looking at AI tools as a casual user. I build products that people depend on. Through ZOEC, I have worked on platforms that support learning across Zambia, and eskulu alone has helped reach more than 500,000 students with notes, past papers, marking schemes, quizzes, and AI-powered learning support. When you are building systems at that scale, the quality of your coding assistant matters.

It affects:

• how fast you can prototype features,
• how accurately you can debug code,
• how well the model understands large codebases, and
• how reliably it reasons through technical problems.

For me, this is not about hype. It is about productivity, quality, and the ability to ship better technology from Zambia into the wider African market.

Claude is currently stronger on coding benchmarks

One of the clearest reasons I am recommending Claude is that its current models are performing better on online benchmarks. Based on what I have observed, Claude 4.5 Sonnet and 4.6 Opus are beating the current OpenAI models for coding-related performance.

Benchmarks are not everything, but they do matter. They give us a structured way to compare how models perform on programming tasks, reasoning challenges, and technical problem-solving. If one family of models is consistently leading there, it is worth paying attention.

As someone who has worked across engineering, AI, and product development, I have learned that the best tool is not always the most popular one. The best tool is the one that helps you solve real problems better.

Context size changes everything

Another major advantage is context size. In coding, context is critical. A model is much more useful when it can keep track of larger files, multiple functions, long conversations, documentation, and architectural patterns without losing the thread.

That matters even more when you are working on serious products rather than isolated code snippets.

For example, when building education technology in Zambia, I often think beyond a single prompt. I think about the whole system:

• the backend logic,
• the frontend experience,
• the AI layer,
• curriculum structure,
• student usability, and
• deployment constraints in our local environment.

A model with a larger context window can follow that broader picture much better. It can stay grounded in the project requirements and produce outputs that are more aligned with the actual system you are building.

Reasoning ability is where the gap becomes obvious

In my experience, the biggest difference shows up in reasoning ability. Coding is not just about generating syntax. Real software development requires structured thinking.

You need a model that can:

• trace logic through multiple steps,
• identify hidden errors,
• understand trade-offs,
• suggest cleaner architecture, and
• work through edge cases carefully.

This is where Claude has stood out for me. After running my own tests, I found it to be much better. It handled coding tasks with stronger reasoning and better overall performance, which is why I am making the switch myself.

That does not mean every output will always be perfect. No model is perfect. But if one system consistently reasons better, that advantage compounds over time, especially for developers and founders who are building every day.

Why African developers should pay attention

Across Africa, we are in a very important moment. More developers, founders, and startups are building local solutions for education, finance, health, agriculture, logistics, and public services. In that environment, access to better AI coding tools can make a real difference.

We do not always have the luxury of large engineering teams or endless funding. Many of us are building lean. We are testing ideas quickly, wearing multiple hats, and solving local problems with limited resources. A stronger coding assistant can help close that gap.

That is one reason I care deeply about these comparisons. Better tools help us build faster and smarter here in Zambia and across the continent.

My own journey has been shaped by that mindset. I started coding in Grade 12, built platforms that now reach hundreds of thousands of users, and have continued teaching myself across AI, software engineering, and product development. Along the way, I was honored to win Business With a Purpose at the X Pitchathon by Accessbank & MTN in 2023, and those kinds of milestones only reinforced something I already believed: African builders must stay close to the best tools available.

This is not about loyalty to a brand

One mistake people make in tech is becoming emotionally attached to tools. I do not think that is the right approach. As engineers and founders, we should stay practical.

If a better model comes out tomorrow, I will test it. If another platform becomes stronger for coding, I will use it. What matters is performance.

Right now, based on what I have seen, Claude is ahead for coding. It is leading on benchmarks, it offers a larger context window, and it shows stronger reasoning. That combination makes it a better choice for my workflow at this moment.

How I’m thinking about this in my own work

As I continue building through ZOEC and expanding AI systems for education and business, I want tools that help me move with more precision. Whether I am improving eskulu, exploring AI integrations, or working on consulting projects through MAY and Company, I need models that can handle technical complexity well.

I have also invested heavily in sharpening my own AI engineering skills, including certifications such as GPT-4 Foundations: Building AI-Powered Apps and AWS Lambda Foundations. That technical lens makes me look beyond marketing. I care about what actually works in development environments.

And right now, Claude is giving me better results.

Final thoughts

If you are a developer, founder, student engineer, or technical builder in Zambia or anywhere in Africa, my advice is straightforward: test Claude seriously for your coding workflow. Do not rely only on brand familiarity. Compare outputs. Check reasoning. Test large-context tasks. See which tool helps you build better.

For me, that decision is now clear. I am switching.

If you are interested in AI-powered education, you can explore eskulu and see the kind of work I am building for Zambian learners. And if you need help with AI consulting, software development, generative AI integration, or building AI-powered platforms, feel free to reach out to me at jeffmdala@gmail.com.

Africa needs more builders using the best tools available. I intend to keep doing exactly that.