OpenAI, Claude or Llama 3: how to choose an LLM for your product

How do you choose the right LLM for a product?

You choose the right large language model (LLM) by testing a few candidates on your own task and comparing quality, cost, speed and constraints. Public leaderboards are a useful first filter, but they measure general skill on generic questions. Your product has its own documents, users and edge cases, and only those show which model actually works best.

The three families most teams shortlist are OpenAI's models, Anthropic's Claude, and Meta's Llama 3. I work with all three, including as an OpenAI & Prompt Engineer at Xobot and in the no-code AI bot framework I built, where users build agents on configurable LLMs, including OpenAI and Llama 3. The process below is the one that holds up across them.

What are the main differences between OpenAI, Claude and Llama 3?

The biggest difference is how you run them, not raw intelligence. OpenAI and Claude are hosted APIs: you send a request, the provider runs the model, you pay per token. Llama 3 is an open-weight model: you can download it and run it on your own hardware or through a hosting provider.

Model names, prices and limits change with every release, so treat this table as a way to think, not as a spec sheet. At the time of writing (September 2026), current Claude models accept up to a 1M-token context window, which suits products that work over long documents; check each provider's model list for today's numbers before you decide.

Which criteria matter most?

Start from what would make the product fail, then rank the criteria. For most products the order is:

1. Quality on your task. Does it answer correctly, follow the format, and say "I don't know" when it should?
2. Cost per completed task. Measure the full cost of getting a good result, including retries and extra turns, not the price per million tokens.
3. Latency. Time to first word matters for chat; total time matters for background jobs.
4. Constraints. Data residency, compliance, offline use, or a customer's rule against third-party processing can rule a model out before quality is even tested.
5. Features. Tool use for agents, structured output for pipelines, vision for images, streaming for chat, long context for large documents.

How do you test models on your own task?

Build a small evaluation set (a fixed list of test cases) before you compare anything:

1. Collect 50 to 100 real inputs from your product or its closest equivalent: support questions, documents, forms.
2. Write down what a good answer must contain, or must not contain, for each one.
3. Run every candidate model on the same set with the same prompt.
4. Score the results with simple checks where possible and a rubric where not, then compare quality and cost side by side.

A minimal harness can be provider-neutral. Each model sits behind the same function, so the test loop never changes:

from typing import Callable

TestCase = dict  # {"input": str, "must_contain": list[str]}

def score(ask: Callable[[str], str], cases: list[TestCase]) -> float:
    passed = 0
    for case in cases:
        answer = ask(case["input"]).lower()
        if all(term.lower() in answer for term in case["must_contain"]):
            passed += 1
    return passed / len(cases)

cases = [
    {"input": "What is your refund window?", "must_contain": ["30 days"]},
    {"input": "Do you ship to Canada?", "must_contain": ["yes"]},
]

# ask_openai, ask_claude and ask_llama each wrap one provider's SDK call.
# for name, ask in {"openai": ask_openai, "claude": ask_claude, "llama": ask_llama}.items():
#     print(name, score(ask, cases))

Real evaluations need more than keyword checks, but even this catches the biggest differences. The article on testing LLM apps covers rubrics and regression runs in detail.

Should you fine-tune, add RAG, or just switch models?

Before swapping models, check whether the problem is the model at all. If answers are wrong because the model lacks your company's facts, add retrieval-augmented generation (RAG) so it can read your documents; see what RAG is. If answers have the right facts but the wrong style or format, better prompts or fine-tuning may help; see RAG vs fine-tuning. Switching models fixes quality gaps in reasoning and instruction following, not missing knowledge.

How do you keep the choice reversible?

Put every model call behind one small function or class in your code, such as generate_reply(messages). Keep prompts in version control, and keep provider-specific details (SDK calls, message formats, tool definitions) inside that wrapper. When a better or cheaper model ships, you change one module and re-run the eval set.

Also plan for the fact that providers retire old model versions. Pin the exact model ID you tested, note the date, and schedule a re-test when a new version arrives.

Summary

There is no single best LLM, only the best one for your task, budget and constraints. Shortlist hosted APIs like OpenAI's models and Claude for speed and quality, add Llama 3 when control over data and hosting matters, then let a small eval set of real examples decide. If you want help picking and wiring up the right model, see AI applications.