You’re reviewing a resume and the candidate lists “experience with large language models” or “proficiency with Claude and GPT.” Do you know what that means? Do you know the difference? Does it matter for the role you’re filling?

If you’re a hiring leader without a deep technical background, you’re not alone in feeling uncertain. The AI model landscape has exploded, and keeping track of who built what — and why it matters — is genuinely hard.

This guide is for you. No jargon. No hype. Just what you need to know to make better hiring decisions.

The Major Players

Claude (Anthropic)

Anthropic is an AI safety company founded by former OpenAI researchers. Their model, Claude, has become the go-to choice for many enterprises that prioritize safety, accuracy, and long-form reasoning. Claude is known for producing careful, well-structured outputs and handling complex instructions with nuance.

Where it shows up in enterprise: Legal document analysis, financial research, internal knowledge management, code generation, and customer-facing applications where accuracy matters more than speed.

What it means on a resume: A candidate who’s worked with Claude likely has experience in settings where precision and safety guardrails matter — often enterprise or regulated environments.

GPT-4o (OpenAI)

OpenAI is the most recognized name in AI, largely because of ChatGPT. GPT-4o is their flagship model, capable of processing text, images, and audio. It’s the Swiss Army knife of AI models — broadly capable and widely deployed.

Where it shows up in enterprise: Content generation, customer support automation, data analysis, coding assistance, and multimodal applications that combine text with images or voice.

What it means on a resume: GPT experience is the most common. It tells you the candidate has engaged with AI, but it doesn’t by itself signal depth. Ask what they built with it.

Gemini (Google)

Google’s Gemini models are tightly integrated with Google’s ecosystem — Workspace, Cloud, Search. Their strength is in applications that need to work across Google’s product suite and in scenarios requiring real-time information retrieval.

Where it shows up in enterprise: Companies heavily invested in Google Cloud Platform, applications requiring search integration, and multimodal use cases that leverage Google’s data infrastructure.

What it means on a resume: Often signals the candidate works in a Google-centric environment. Worth asking about the specific Gemini applications they’ve used.

Llama (Meta)

Meta’s Llama models are open-source, which means anyone can download, modify, and deploy them. This is a fundamentally different model from the others on this list — it’s not a service you pay for, it’s software you run yourself.

Where it shows up in enterprise: Companies that need to run AI models on their own infrastructure for privacy, compliance, or cost reasons. Common in healthcare, defense, financial services, and any organization that can’t send data to external APIs.

What it means on a resume: A candidate with Llama experience likely has deeper technical chops. They’ve dealt with model deployment, infrastructure, and potentially fine-tuning — not just prompting.

Mistral

Mistral is a French AI company that has gained a strong reputation for efficient, high-performance models. Their models punch above their weight — delivering strong results with less computational overhead than larger competitors.

Where it shows up in enterprise: Cost-conscious deployments, European companies with data sovereignty requirements, and applications where inference speed and efficiency matter.

What it means on a resume: Signals technical sophistication and awareness of the broader AI ecosystem beyond the big American players.

What Actually Matters for Hiring

Here’s the honest truth: for most non-technical roles, the specific model a candidate has used matters less than how they’ve used it. The questions that reveal real capability are:

“What did you build or automate with AI?” — You want specific use cases, not general familiarity.

“How did you evaluate whether the AI output was good enough?” — This tells you about judgment, not just tool usage.

“What didn’t work, and what did you do about it?” — This separates people who’ve genuinely worked with AI from those who’ve only experimented casually.

“How did you decide which model or tool to use?” — This is the gold standard question. If a candidate can articulate why they chose Claude over GPT for a specific task — or vice versa — they have real working knowledge.

The Practical Takeaway

You don’t need to become an AI expert to hire well in 2026. But you do need to understand the landscape well enough to ask informed questions. When a candidate says “AI experience,” don’t nod and move on. Ask which tools, what outcomes, and what they learned.

The best candidates will appreciate the question. The ones who can’t answer it in specifics probably aren’t as fluent as their resume suggests.

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Scherer Talent is a boutique recruiting firm based in Austin, TX. We specialize in digital transformation, technology, and leadership roles. Schedule a call to discuss your search.