Which AI Model Should Marketers Actually Use?

This article is regularly updated as new models are released. AI moves fast, and your toolkit should too.

Table of contents

How the providers differ

The four providers are converging on capability (1M context windows, multimodal input, agentic tool use are now table stakes), but they remain genuinely different in character. The shorthand:

• OpenAI (ChatGPT). The product company. Biggest consumer install base, deepest plugin and integration ecosystem, and the most polished agentic experience with GPT-5.5. If your team needs one tool that “just works” across research, drafting, and tool use, this is the safe bet.
• Anthropic (Claude). The writing and brand company. Strongest prose, cleanest tone control, and the only major lab that doesn’t train on your data by default. Favoured by marketing teams handling sensitive briefs, pre-launch strategy, or copy that has to sound human.
• Google (Gemini). The multimodal and data company. Best at video, images, and structured data. Tight integration with Google Workspace makes it the natural pick if you live in Sheets, Docs, and YouTube.
• Meta (Llama). The open weight company. Massive context, very low cost per token, and the only option you can self-host. Wildcard for technical teams and high-volume workloads where data control or unit economics matter more than polish.

Proprietary vs open weight

Every model on the market falls into one of two camps.

Proprietary models are closed weights accessed through a hosted API: OpenAI, Anthropic, Google DeepMind, xAI, Cohere, Amazon Nova, Mistral’s commercial tier. You rent capability with no infrastructure to run, but pricing, rate limits, and data policies can change without your input.

Open weight models are published so you can self-host or use a hosted provider (Together AI, Fireworks, Groq, OpenRouter, DeepInfra). Major families: Llama (Meta), Gemma (Google), Kimi (Moonshot), DeepSeek, Qwen (Alibaba), Mistral’s open releases, Falcon (TII). They give you three things proprietary models can’t:

• Full data control. Nothing leaves your environment
• Predictable cost. Typically 10-30x cheaper than equivalent proprietary tiers (see The cost gap)
• No surprise deprecations. The model you build on today is the model you’ll have in two years

The old engineering objection has largely gone: hosted providers offer all the major open weights behind a standard API, often with faster inference than the proprietary labs.

For marketing teams in 2026, the honest answer is to route by job. Pay the proprietary premium where prose quality, agentic reliability, or specific capabilities (Deep Research, video, brand voice) earn it. Use hosted open weights for everything else. The mix shifts open every quarter.

The cost gap

Output pricing is what makes proprietary models brutal, not input. Across every proprietary provider, output rates run 4-6x input rates: Claude Opus 4.7 charges $5/M input but $25/M output; GPT-5.5 charges $5/M input but $30/M output. For workflows that generate text rather than just classify it, that ratio decides your unit economics.

A typical chat turn (5K input tokens, 1K output) costs roughly:

• Llama 3.3 70B on Groq: $0.0037 (a third of a cent)
• Claude Sonnet 4.6: $0.030 (3 cents)
• Claude Opus 4.7: $0.050 (5 cents)
• GPT-5.5: $0.055 (5.5 cents)

At a million chat turns a month (the kind of volume a content engine or classification pipeline burns through quickly), that’s roughly $3,700 vs $50,000-$55,000. Output-heavy workflows (long article drafts, analysis reports, agentic loops) widen the gap further.

The best AI models for marketers

If you only want one view, here are the top picks from each camp side by side. The proprietary models lead on polish, agentic reliability, and consumer-facing UI. The open weight models lead on cost (often 10-50x cheaper per token) and on raw context windows. Scan the table, then dive into the dedicated sections below.

Best model by marketing use case

Choosing a proprietary model for MCP-heavy marketing work

Anthropic invented the MCP protocol and open-sourced it in late 2024, which gives Claude models a structural edge for tool-calling reliability. Practical picks:

• Claude Opus 4.7. Best for complex multi-step agentic flows where each tool call matters. Leads the LM Arena coding leaderboard at 1567 Elo, which translates well to tool-use precision.
• Claude Sonnet 4.6. Sweet spot for high-volume MCP work. Roughly a third the cost of Opus, still highly reliable on structured tool calls.
• GPT-5.5. Pick this if you need OpenAI’s broader ecosystem (Deep Research, Code Interpreter, ChatGPT plugins). Designed end-to-end for agentic tasks across tools.
• Gemini 3.1 Pro. Pick this if your MCP servers return rich multimodal payloads (charts, screenshots, video frames).

Choosing an open weight model for MCP-heavy marketing work

Tool-calling reliability varies more across open weight models than proprietary, and benchmarks don’t always reflect production behaviour. Practical picks:

• Kimi K2.6. Strongest open weight choice for multi-step agentic flows. Specifically positioned for tool use, leads Humanity’s Last Exam (with tools) at 54%, with a 256K context for long tool histories.
• Llama 3.3 70B on Groq. Best for simple single-call automations at high volume. Mature tool-use support across MCP clients, fast inference, very cheap output ($0.79/M).
• DeepSeek V3. Avoid for MCP-heavy work. Practitioners report less reliable structured tool calling than Kimi or Llama. Better suited to non-tool drafting and summarisation where its prose-per-dollar is unbeatable.

Evaluating the best AI models for marketers

There is no single benchmark designed specifically for marketing quality. Brand voice, persuasion, and audience fit are subjective and brand-dependent, so the field hasn’t standardised. Instead, you triangulate across a few standards. Here’s how the top picks compare across the benchmarks worth bookmarking:

May 2026 snapshots from LM Arena, AAII, and llm-stats.com. Scores shift weekly. Em-dashes mean not in current snapshot; ”~” means previous-version proxy.

• LM Arena Elo: human preference across proprietary and open weight models. The Creative Writing sub-leaderboard is the most marketing-relevant slice (Claude dominates).
• AAII: composite intelligence score blending MMLU-Pro, GPQA Diamond, MATH, HumanEval. Best when charted against price.
• IFEval: does the model follow your instructions? The single most relevant benchmark for marketing briefs.

Most benchmarks aren’t built for marketers. AAII and SWE-Bench dominate model launch posts and press coverage, but they measure things marketers rarely need: graduate-level reasoning, competition math, software engineering. Llama 4 Maverick scoring 18 on AAII doesn’t mean it can’t write a LinkedIn post — it means it can’t solve graduate physics problems. Most marketing work (drafting posts, generating ad variants, writing intros, summarising interviews) doesn’t need hard reasoning or production coding. For prose and brief-following, IFEval and LM Arena Creative Writing are the more honest signals.

The best proprietary AI models for marketers

The proprietary frontier in May 2026 is a three-horse race: OpenAI’s GPT-5.5, Anthropic’s Claude Opus 4.7, and Google’s Gemini 3.1 Pro. All three sit within touching distance on the headline benchmarks (LM Arena, Artificial Analysis, llm-stats) and any of them is a reasonable default for a marketing team. Differences show up at the edges: writing quality, agentic reliability, multimodal range, data policies, and price.

GPT-5.5 launched on 23 April 2026 and is now the default in ChatGPT, focused on end-to-end agentic tasks. Claude Opus 4.7 followed on 16 April and currently leads the LM Arena coding leaderboard at 1567 Elo. Gemini 3.1 Pro launched on 19 February with the strongest multimodal capability, scoring 77.1% on ARC-AGI-2 and 80.6% on SWE-Bench Verified. Anthropic remains the only major lab that doesn’t train on your data by default, worth weighing for pre-launch or sensitive work.

Pricing sourced from each provider’s official pricing pages (OpenAI, Anthropic, Google AI). Note: Opus 4.7’s per-token rates match 4.6, but a new tokenizer can produce up to ~35% more tokens for the same input. GPT-5.5 also charges 2x input and 1.5x output on prompts above 272K tokens.

The best open weight AI models for marketers

The open weight scene moves faster than the proprietary frontier and is closing the quality gap with each release. As of May 2026, the serious choices for marketing teams are Llama (Meta), DeepSeek, Kimi (Moonshot AI), Qwen (Alibaba), and Gemma (Google). All are accessible without any infrastructure work via hosted providers like Together AI, Fireworks, Groq, OpenRouter, and DeepInfra.

April 2026 was a heavy release month: Llama 5 (8 April) with a 5M-token context, Gemma 4 (2 April) for self-hostable reasoning, Kimi K2.6 (20 April) which ties GPT-5.5 on SWE-Bench Pro at 58.6%, and Qwen 3.6-27B (22 April) hitting 77.2% on SWE-bench Verified under Apache 2.0. DeepSeek R1 remains the reference reasoning model at 79.8% AIME 2024 and 97.3% MATH-500.

Pricing varies by hosting provider. Sourced from pricepertoken, llm-stats, and OpenRouter.

Honourable mentions: Mistral continues to release strong open weight models alongside its proprietary tier, useful for European teams with data residency requirements. Falcon (TII, UAE) and Z.AI’s GLM-5 round out the credible open weight roster. xAI’s Grok 4 sits on the proprietary side but is increasingly competitive on reasoning benchmarks.

How to choose

Mollick’s advice for anyone using AI seriously:

“For most people who want to use AI seriously, you should pick one of three systems: Claude from Anthropic, Google’s Gemini, and OpenAI’s ChatGPT.”

And on picking the right model tier:

“The casual models are fine for brainstorming or quick questions. But for anything high stakes (analysis, writing, research, coding) usually switch to the powerful model.”

For most marketers, the practical approach is:

1. Pick one primary model for day-to-day work (Claude Sonnet or GPT-5.5 are both strong defaults)
2. Use a secondary model for specific use cases where another provider has a clear edge (e.g. Gemini for video, Claude for long-form)
3. Don’t over-optimise model selection. Clean, connected data matters more than which model you use

The releases are coming fast. In April 2026 alone we saw Claude Opus 4.7, GPT-5.5, Llama 5, and Gemma 4. Expect this article to keep changing.