Every founder building an AI product hits this question early. The wrong answer costs you weeks of rework. Here is what actually matters when you are choosing.
The honest answer: it depends on the job
There is no universally best LLM. There is only the right one for your specific use case, budget, and timeline. The mistake most teams make is picking based on hype or habit instead of fit. GPT-4o is not always better than Claude. Claude is not always better than Gemini. Context is everything.
Here is how we think about it at Novion when we are scoping a new product.
GPT-4o: the safe default with the best ecosystem
OpenAI has the widest library of tooling, the largest developer community, and the most mature API. If you are building something fast and you need reliable documentation, examples, and third-party integrations, GPT-4o is the lowest-friction starting point.
It handles general-purpose tasks well. Summarisation, extraction, classification, Q&A, code generation. It is not always the best at any single thing, but it is rarely bad at anything. That consistency matters when you are shipping an MVP and cannot afford to babysit your AI layer.
Where it struggles: long documents, subtle reasoning chains, and tasks that need the model to hold a lot of context without losing the thread.
Claude: the best for long context and nuanced writing
Anthropic's Claude is the one we reach for when the task involves large documents, complex instructions, or outputs where tone and precision matter. It has a 200k token context window in its latest versions. That is not a small detail. It means you can feed it an entire codebase, a long legal document, or a full product spec and get a coherent response.
For products like topapplicant, where the AI needs to read a full resume and job description together and give specific, useful feedback, Claude's ability to reason across a long context without degrading is a real advantage.
It also tends to follow nuanced instructions more reliably than GPT in our experience. If you are building something where prompt discipline matters, Claude rewards the effort.
Where it struggles: it can be slower, the pricing at the top tier is not cheap, and the tooling ecosystem is smaller than OpenAI's.
Gemini: the one to watch, especially if you are in Google's stack
Google's Gemini models have improved significantly. Gemini 1.5 Pro has a 1 million token context window, which is genuinely useful for certain use cases. If you are working with large codebases, hours of transcripts, or massive structured datasets, that window is hard to ignore.
Gemini also integrates tightly with Google Cloud, Workspace, and Vertex AI. If your infrastructure is already in GCP or your customers live in Google's ecosystem, the native integrations reduce friction in ways that matter.
For general product building, Gemini is not our first pick yet. The output quality on nuanced writing and reasoning tasks still sits behind GPT-4o and Claude in most of our tests. But it is closing the gap fast, and the multimodal capabilities are strong.
Where it struggles: consistency on complex prompts, and a developer experience that still feels rougher than OpenAI's.
How to actually decide
Ask yourself four questions.
First, what is the primary task? Writing and long documents lean toward Claude. General-purpose tasks lean toward GPT-4o. Large-scale data or Google stack lean toward Gemini.
Second, how long is the context you need to process? Under 32k tokens, any of them work. Over 100k tokens, Claude or Gemini 1.5 Pro are your real options.
Third, how fast do you need to ship? If speed matters and you have never used any of them before, start with OpenAI. The documentation and community will save you hours.
Fourth, what does it cost at scale? Run the numbers on your expected usage before you commit. Pricing models differ in ways that compound quickly once you have real traffic.
The other thing worth saying: you are not married to one. Most production products end up using two or more models, routing different tasks to different providers based on cost and performance. Build your integration layer to be swappable from day one.
If you are not sure which direction makes sense for what you are building, that is exactly the kind of decision we help founders make quickly.
Book a free call at novion.one and we will help you pick the right stack before you write a single line of code.