Should You Still Trust LLM Benchmarks?

Can You Evaluate an AI with a Quiz?

Imagine a student who only studied one chapter for the exam.
On test day, that chapter is exactly what comes up. The student scores 18/20.

Are they good?
Sure — but only for that chapter. Their broader skills, reasoning, and adaptability? Still unknown.

This is exactly the kind of bias introduced by SimpleQA, a benchmark now widely used to compare LLM performance.

Launched by OpenAI in late 2024, SimpleQA contains thousands of short factual questions with one correct answer. LLMs are scored based on whether they get it right.

Clear. Consistent. Quantifiable.

But in a business context? That kind of evaluation falls short.

Can you really choose a strategic AI model based on a general knowledge quiz?
That’s the question we asked ourselves — and answered differently at Starzdata.

What SimpleQA Measures — and What It Doesn't

SimpleQA, published by OpenAI in 2024, quickly became a standard in LLM comparisons.
Its premise: over 4,000 factual questions, covering capital cities, dates, names, formulas. Every model is tested on its ability to answer them correctly, Q&A style.

📊 Simple. Stable. Quantifiable.
Very useful for ML engineers or academic comparisons.
But it only measures one type of intelligence: factual memory.

🔗 Official paper: Measuring short-form factuality in large language models (OpenAI, 2024)

Major Biases to Know

1. Topic Bias

The questions heavily focus on music, sports, geography, gaming. Very little reflects B2B, technical, or strategic use cases.

2. Optimization Bias

Some models are tuned on the benchmark itself. They perform well on the test, but lack generalization.

3. No Reasoning

SimpleQA does not measure reasoning, justification, or uncertainty handling.

🔗 SimpleQA Verified: improved diversity & labeling (2025)

Bottom Line

SimpleQA measures factual recall. But what businesses need from an LLM is:

• Framing ambiguous problems

• Providing contextualized responses

• Explaining reasoning, even admitting uncertainty

No quiz can capture that.

Tavily, Linkup, Perplexity: The Benchmark Race

As SimpleQA becomes a standard, vendors compete for the highest scores. Tavily, Linkup, Perplexity... each claims superior performance.

But here’s the key question:
👉 Do those scores actually help you make a business decision?

Recent Results (2024–2025)

🔗 Tavily vs Perplexity vs EXA vs Google

What These Scores Don’t Show

• Models are tuned for the test, not real-world scenarios

• They rarely manage uncertainty or say “I don’t know”

• Benchmarks ignore business context, segmentation, or KPIs

What Really Matters

What you care about is not whether a model can answer “What’s the longest river in the world?”

You care about:

• Can it segment your market?

• Justify pricing hypotheses?

• Spot intent signals in your pipeline?

Public benchmarks don’t test that today.

Why Enterprises Need a Different Method

Choosing an AI model isn’t academic.
It’s a business decision with impact on cost, margin, productivity, and risk.

Benchmarks like SimpleQA don’t let you:

• Test your real questions

• Assess nuanced, explainable answers

• Measure value against your KPIs

You’re not hiring an LLM to win a quiz.

You’re hiring it to:

• Prioritize CRM accounts

• Structure your GTM segmentation

• Spot opportunity signals across your market

Benchmarks don’t help with that. A reasoning engine does.

The Starzdata Approach: Reasoning + Plurality

At Starzdata, we evaluate LLMs differently:
We test them on real business problems, let them debate, score each other, and extract the most justified answer.

How it works

1. A real-world business question is defined

2. Multiple LLMs respond, rate and review each other

3. One answer is selected, scored, documented

Every datapoint is:

• Explainable

• Auditable (agreement score, warning flags)

• Activable (usable in CRM, CMS, campaigns)

• Contextualized (sector, segment, tone, language)

Use Cases from Clients

Clients use Starzdata Magic Segments to:

• Map the market: cluster sector/geographic trends to drive product or R&D direction

• Activate GTM: prioritize high-potential accounts for Paid or Outbound (e.g., “rich but digitally underserved” companies)

• Optimize CRM: enrich incomplete records with verified data, public signals, and digital maturity scoring

Delivered, tested, and scored — in under 72 hours.

Conclusion — Better Than a Quiz: Reasoning

Public benchmarks are useful.
But they don’t replace an evaluation focused on reasoning, traceability, and business value.

A useful model:

• Explains its logic

• Handles uncertainty

• Adapts to your operational context

• Produces reliable, actionable outputs

That’s what we deliver every day at Starzdata.

🧠 Want to test a real-world challenge in your context?