LLMs for marketers: 6 AI models for every modern marketer

LLMs (Large Language Models) for marketers are becoming essential as AI reshapes how research, content, and personalization are done in modern marketing. Instead of relying solely on manual brainstorming or slow content workflows, marketers can now tap into AI models that summarise data, generate ideas, refine messaging, and accelerate production.

In this article, we break down six LLMs every marketer should understand; what they do, where they excel, and how they can fit into a B2B workflow.

1. Claude (Anthropic)

The model with a conscience and a memory

Claude, developed by Anthropic, is the most “humanlike” model on the list. It’s best known for its ability to reason, reflect, and summarize long documents with incredible nuance and clarity. Claude 3 Opus (its most powerful version) excels in tasks that require long-form understanding and ethical grounding.

Strengths for Marketers:

Claude shines when you’re trying to:

• Summarize complex reports or transcripts (like webinar recordings or whitepapers)
• Draft strategic narratives or thought leadership
• Conduct nuanced competitor analysis
• Maintain tone and structure in long-form brand content

Claude is the go-to for teams that value trustworthy, context-aware output, especially in sensitive industries like healthcare, legal, or fintech.

It’s also notably strong at aligning with brand voice and being cautious with hallucinations (fabricating information). That makes it a great tool for CMOs who need quality over speed.

Limitations:

Claude is a bit slower than others, and sometimes too cautious. It might over-hedge language (“it’s possible that…”), especially if your prompts are unclear. It’s also not ideal for generating lots of short-form outputs quickly.

2. GPT-4o (OpenAI)

The all-around MVP

GPT-4o, OpenAI’s latest flagship model, is the most widely adopted—and for good reason. It’s fast, multimodal, and incredibly versatile. It can reason, write, code, summarize, translate, and even interpret images or audio.

GPT-4o is your Swiss Army knife. It works for 90% of use cases across copywriting, landing page generation, email personalization, and even technical tasks like schema markup or structured data output.

Strengths for Marketers:

• High-quality short- and long-form content generation
• Brainstorming and ideation
• A/B testing copy variants
• Generating structured outputs (like JSON or HTML)
• Playing nicely with tools like Clay, Zapier, and LiftPilot

GPT-4o is especially useful for marketers automating large content workflows or outbound campaigns. It’s what powers most generative ABM systems (including LiftPilot), because it strikes the best balance of intelligence, speed, and formatting fidelity.

Limitations:

While GPT-4o is broadly strong, it doesn’t have the same “moral compass” or interpretive sensitivity as Claude. It may hallucinate facts, and it’s less trustworthy for legal, financial, or regulatory content unless you pair it with verification logic.

3. Gemini (Google)

The research king with real-time data access

Gemini is Google’s multimodal, multi-device LLM platform. It’s designed to integrate seamlessly across Google Workspace, making it ideal for teams that live in Docs, Sheets, Gmail, and Meet.

Where Gemini shines is real-time research, live data awareness, and context fusion. Want to pull the latest stats from a live Google Sheet, summarize an email thread, and draft a deck in one flow? Gemini can do that.

Strengths for Marketers:

• Live content planning using search data
• Google-native workflows (like turning Google Form responses into presentations)
• Collaborating on documents with dynamic brand input
• Research-heavy blog content, citations, and real-time trend reporting

If your team relies on Google tools, Gemini becomes a no-brainer. It integrates across your workspace and helps bridge the gap between your data and your output.

Limitations:

Gemini isn’t yet as creative or polished as GPT-4o or Claude when it comes to tone and storytelling. It also performs inconsistently when pulled outside the Google ecosystem.

4. Mistral

The open-source engine for scale and speed

Mistral is the LLM of choice for developers, automation architects, and privacy-first marketers. It’s fast, efficient, and open, which means you can self-host it, fine-tune it, and use it for custom applications without vendor lock-in.

For marketers building custom outbound or personalization workflows at scale, especially via tools like Clay or internal automation stacks—Mistral is the infrastructure layer that lets you go far beyond one-off prompts.

Strengths for Marketers:

• Fast, low-cost short-form output (snippets, tags, subject lines)
• Integrates easily with outbound workflows
• Can be fine-tuned for specific brand or product use cases
• Private, compliant environments for sensitive data

Mistral is what your RevOps engineer reaches for when you want a bespoke personalization engine that runs in your infrastructure—not someone else’s cloud.

Limitations:

Mistral’s default outputs aren’t as nuanced or refined as GPT or Claude. It works best when you know what you want, and have a dev team to guide it. Not plug-and-play for non-technical marketers.

5. LLaMA (Meta)

Built for control and customization

LLaMA is Meta’s open-weight LLM project. Like Mistral, it’s meant to be integrated, not just used. Where it stands out is in controllability and fine-tuning flexibility. You can mold it to sound like your brand, understand your product, or follow your campaign logic – if you have the infrastructure to do so.

It’s ideal for enterprise teams building proprietary marketing engines or tools that embed AI deeply into their stack.

Strengths for Marketers:

• Highly customizable for internal use cases
• Powerful when combined with RAG (retrieval-augmented generation)
• Works well for multilingual content or global brand localization

Some enterprise ABM teams are building entire intent-to-asset generation pipelines powered by LLaMA and embedding it inside their CRM or CMS.

Limitations:

Like Mistral, LLaMA requires technical scaffolding. It’s not a standalone productivity tool. Without fine-tuning, its outputs can be generic or inconsistent.

6. Groq (LLM Hardware Accelerator)

Not a model, but the speed multiplier every workflow needs

Groq isn’t an LLM itself, it’s a chip architecture built specifically for LLM inference. In other words, it makes AI models run insanely fast.

Groq can generate hundreds of tokens per second, which makes it perfect for:

• Real-time personalization
• Instant AB testing generation
• Live chat responses
• Voice-to-text workflows

If you’re using LLMs in a dynamic outbound workflow (e.g., creating LiftPilot pages or outbound snippets at scale), Groq-backed infrastructure lets you run LLaMA, Mistral, or Mixtral faster and cheaper than cloud APIs.

Strengths for Marketers:

• Lightning-fast content generation for live workflows
• Reduces latency in outbound automations
• Powers real-time personalization for sales, chatbots, and websites

It’s a back-end accelerator, not a tool you interact with directly—but it’s quickly becoming a critical part of the AI stack for speed-driven GTM teams.

When to use what: The LLM cheat sheet for marketing use cases