Should founders pick one LLM or use several?

Use several, and route each task to the model that is best at it. Because the leading models have distinct strengths and distinct behaviors, locking your whole product to one model leaves quality on the table. The stronger pattern is to keep the model behind a thin seam in your code so you can send backend debugging to one model, frontend work to another, and heavy reasoning to a third, then swap any of them as new versions ship. Discerning teams increasingly pick the best model per task rather than defaulting to one for cost reasons.

Are open source LLMs good enough for startups?

Often yes, with a caveat on timing. Y Combinator's read is that open source models tend to run roughly three to six months behind the proprietary frontier. For many tasks that gap does not matter, and open models are popular precisely because teams do not want to trade away quality or control. A practical approach is to prototype on a strong closed model to move fast, then move suitable workloads to an open model where the quality is close enough and the cost or control benefits are real. Run your own evals to decide, do not guess.

Will LLMs become commoditized?

The leading view among founders is that base models are trending toward commoditization: over time they converge on similar behaviors and compete harder on price. That does not mean models stop mattering, it means the durable advantage moves up a layer. The team that understands its customer deeply and builds the right product on top of these models is the one that wins, not the team that happens to sit on a slightly better base model this quarter. Treat the model as an input you can swap, and build your moat above it.

Best LLM for Founders: How to Choose

Q: What is the best LLM?

There is no single best LLM, and asking the question that way is the mistake. The leading models are genuinely different in what they are good at. In Y Combinator's framing, Anthropic's Claude Opus is a workhorse, OpenAI's Codex is strong at backend debugging, and Google's Gemini is strong on frontend tasks. The best LLM is the one that fits the specific job in front of you, so the right question is not which model wins overall but which model wins for this task. Most serious teams end up using more than one.

Every founder building with AI eventually asks the same question: what is the best LLM? It feels like it should have a clean answer, a single model that beats the others so you can pick it and move on. In a Y Combinator video on the differences between the leading AI models, the answer is more useful than a leaderboard. As the discussion puts it, the leading models "are very different." They are not interchangeable, and treating them as interchangeable is how you leave quality and speed on the table. Here is how to actually choose.

The leading models are genuinely different

Start with the fact that surprises founders who expect one model to dominate: the top models have distinct personalities. Y Combinator's shorthand is that Claude Opus, made by Anthropic, is a workhorse; OpenAI's Codex is strong at backend debugging; and Google's Gemini is strong on frontend tasks. Those are not marketing claims, they are the kind of thing teams notice after running the same job through each model and watching where it shines and where it stumbles.

The differences go deeper than benchmarks. Each model has its own behavior you can learn and lean on. In a separate YC session on running coding agents, one builder described the default model inside Claude Code as behaving like an "ADHD CEO": fast, opinionated, and best when you give it clear direction. That is a personality, not a spec sheet. The point for a founder is that you do not evaluate a model in the abstract, you evaluate it against your task and learn how it actually behaves there.

Ask "best for what," not "best overall"

Once you accept that the models are different, the question "what is the best LLM" quietly falls apart. There is no best model in general, only a best model for a specific job. Backend debugging, frontend scaffolding, long-document reasoning, cheap high-volume classification: these reward different models, and the winner changes by task.

This is why the strongest teams route work across models instead of standardizing on one. The infrastructure builders at Baseten, in a Greylock conversation, made the same observation from the customer side: the most discerning buyers increasingly pick the best model for each specific task and are willing to look past raw cost to get the quality they want. Google's Logan Kilpatrick described the opposite pull from inside a big platform, where "everything is now sort of using Gemini in some way" because it became the through line across more than fifty products. Both are true at once: a large platform standardizes for coherence, while a focused startup routes for quality. As a founder, you are the startup. Route.

Keep the model behind a thin seam

Routing across models only works if swapping a model is cheap. If your product only functions because you hand-tuned prompts and scaffolding around one specific model, you have built a cage, and every new release becomes a rewrite instead of an upgrade. The fix is architectural: keep the model behind a thin seam so that sending a task to a different model, or adopting a better version, is a config change rather than a project.

This is the same discipline behind building for the next AI model, not this one: the models will keep changing under you, so design so the change is a drop-in. A founder who can move a workload from one model to another in an afternoon gets to use the best LLM for every task and gets to absorb every upgrade the day it lands. A founder who cannot is stuck defending a choice they made six months ago.

Open source is close, but a step behind

The next question is usually whether you even need the frontier. Open source models have gotten very good, and for many workloads they are more than enough. Y Combinator's read is that open source models tend to run roughly three to six months behind the proprietary frontier: close, improving fast, but a step back on the hardest tasks.

Founders adopt them anyway, and for good reasons. In a Greylock discussion on what the most demanding AI customers actually want, one operator put it directly: "the testament to how good open source is, guys, is how many people are using open source models, cuz no one wants to make that trade-off." Teams reach for open models to control cost, keep data in house, and avoid being locked to a single vendor. The practical pattern is to prototype on a strong closed model so you move fast early, then move the workloads where quality is close enough onto an open model, using your own tests to confirm the swap does not hurt.

The base model is commoditizing, so build the layer above

Here is the strategic point that should shape how you spend your time. The consensus among these founders is that base models are trending toward commoditization: over time they converge on similar behaviors and compete on price. That does not make models irrelevant, but it does mean sitting on a marginally better base model is not a durable advantage. Most of the economic value in AI still accrues to the frontier labs, so you are not going to out-model them anyway.

What you can own is the layer above. Y Combinator's conclusion is blunt about where the win is: the team "who understands the customer needs really really well and is able to build for that is going to sort of win the space." There is real optionality in the layers you build on top of the models to meet a specific customer need better than a general tool can. One team in a YC talk on building on foundation models noted they ran at "half the cost of Gemini 3 deep think because we were building on top of Gemini 3 Pro," a cheaper base, and getting a better result for their use case through the layer they added. The model was an input they chose deliberately; the product around it was the edge.

That reframes the whole "best LLM" question. The best LLM is not a trophy you win once, it is a swappable input you choose per task. Your moat is the customer understanding and the product on top, which is exactly the operating model we teach founders.

How to actually decide

Turn all of this into a simple decision loop:

List your real tasks. Not "we use AI," but the specific jobs: debug the backend, generate the UI, summarize long documents, classify support tickets. Each is a separate decision.
Try more than one model per task. Run the same real job through two or three models and compare the output on your work, not on a public benchmark.
Measure, do not vibe. Use your own evals so you can say a model is better for your task with evidence, and re-check when a new version ships.
Keep the seam thin. Make model choice a config value so routing and upgrading stay cheap.
Spend your energy on the layer. Put your scarce founder time into understanding the customer and building the product on top, because that is the part competitors cannot copy by swapping a model.

What to do this week

Write down your three most important AI tasks as separate jobs, each with a clear definition of a good result.
For one of them, run the same real input through two different models and compare the outputs side by side.
Check whether your codebase lets you swap models with a config change. If it does not, put a thin seam in front of the model.
Pick one workload and test an open source model against your current closed one to see if the quality is close enough to matter.
Write one sentence on the customer need your product understands better than a general model could, and make sure this quarter's roadmap builds on it.

Choosing the best LLM is not a one-time bet on a winner, it is a habit of matching the model to the task and building your real advantage above it. That habit, using AI without letting any single model own your product, is exactly what we teach in the AI Operating System for Startups.

Sources

Differences Between The Leading AI Models (Y Combinator). The video this article distills: models are genuinely different, base models are commoditizing, open source lags three to six months, and the team that understands the customer wins.
How to Make Claude Code Your AI Engineering Team (Y Combinator). Source for the "ADHD CEO" description of a default coding model's behavior.
The Most Discerning AI Customers Optimize for One Thing: Quality (Greylock). Source for the open source trade-off quote.
The Infrastructure Behind AI Agents with Baseten (Greylock). Source for discerning customers picking the best model per task.
Google's agentic shift with Logan Kilpatrick (Sequoia Capital). Source for Gemini as the through line across Google's products.
The Powerful Alternative To Fine-Tuning (Y Combinator). Source for the half-the-cost example of building a layer on a cheaper base model.
Model makers: Anthropic (Claude Opus), OpenAI (Codex), and Google DeepMind (Gemini).