AI Tools for SEO Work: Cloud Models, Local Models, and What We Actually Use — Comparisons

Most "AI for SEO" content is stuck at "use ChatGPT to write meta descriptions." The real question for serious practitioners is broader: which AI platforms are actually best for the full range of SEO and Answer Engine Optimisation work — auditing, analysis, strategy, code, content? And is there a case for running models locally?

We've built our entire product and content operation across the stack. We use Claude for strategy and code, Gemini for search-heavy workflows, and run Qwen 3.5 122B locally for batch execution. In our testing, the choice isn't about finding the single "best" model — it's about matching the right model to the task.

The Short Answer

Claude is the most capable all-rounder for complex reasoning and creative work. ChatGPT is suitable for quick retrieval and validation. Gemini excels at web search and image generation. Local models are optimal for batch execution and cost-sensitive volume work. The right answer is matching models to tasks, not picking a single winner.

If you are only going to use one platform, Claude is the most capable for serious SEO work. It understands the question behind the question, making it a genuine thinking partner rather than just an output generator.

The Cloud Models: Claude vs ChatGPT vs Gemini for SEO

Selecting a cloud provider depends on the specific workflow. Here's how each platform performs across the key categories of SEO and AEO work.

Complex analysis and strategy

For site audits, competitor analysis, and content strategy planning, Claude is superior. It maintains context across long conversations and understands the underlying question rather than just the literal request. It pushes back when a premise doesn't make sense, which is critical for high-level strategy.

ChatGPT produces competent analysis but tends toward surface-level synthesis. The answers are confident and well-structured, but often follow the letter rather than the spirit of the question. It lacks the depth required for complex strategic reasoning.

Gemini is the weakest option for deep analysis. While it has strong benchmark scores, the real-world performance for complex reasoning is inconsistent. Instruction-following issues make multi-step analysis frustrating; you often spend more time correcting misinterpretations than working on the strategy itself.

Code and technical implementation

For scripts, automation, and technical SEO fixes, Claude (via Claude Code) is the strongest choice. In our own development, over 90% of the codebase is Claude Code-authored. It handles complex multi-file projects and understands codebase context effectively.

ChatGPT's API is solid for building automated workflows and tools. It is competent at code generation and reliable for integration tasks where the logic is well-defined.

Gemini is functional but less reliable for complex implementation. It is better suited for simple scripts than production-grade code where context and maintainability are priorities.

Research and web retrieval

Gemini is best-in-class for web retrieval and search. Built on Google's infrastructure, it provides superior access to real-time data and search results, which is expected but essential for competitor research and SERP analysis.

ChatGPT Search is a solid alternative for real-time retrieval, often producing well-summarised results.

Claude's web access is capable but not as deep as Gemini's for pure search tasks. It is better suited for synthesising retrieved information than gathering it.

Content creation and planning

For content that requires understanding audience, context, and strategic positioning, Claude is the strongest. It grasps what a piece needs to achieve, not just what it needs to say.

ChatGPT produces well-structured content quickly. It is efficient for first drafts where speed matters more than nuanced strategic depth.

Gemini is competent, but the instruction-following issues make iterative content work painful. It may claim changes were made when the output remains identical.

Image generation

For visual mockups, social media graphics, and presentation visuals, Gemini (Imagen) is far ahead of the competition. It is genuinely impressive for visual work, while the other platforms are not significant competitors in this specific space for SEO-relevant tasks.

The Case for Local Models

Why would an SEO team run AI models locally? Three primary drivers: economics, control, and workflow efficiency.

Economics

A dedicated AI workstation costs £3-4,000 upfront. By contrast, Claude Pro costs £90+/month, plus API costs for any automated workflows. If you are doing volume work — batch content analysis, data processing, automated audits — the local model pays for itself quickly. No per-token costs, unlimited usage.

Control and customisation

Local models can be fine-tuned, connected to your own knowledge bases, and run custom workflows without sending data to external APIs. For agencies handling sensitive client data, or companies with intellectual property concerns, this removes the third-party data processing question entirely.

The workflow that actually works

Our production workflow leverages a "frontier + local" division. A frontier cloud model (Claude Opus 4.6) designs detailed briefs with full strategic context. A local model (Qwen 3.5 122B on Grace Blackwell GB10) executes those briefs at scale — turning each brief into a draft in roughly four minutes. The frontier model does the thinking; the local model does the doing.

This pipeline has produced 37 glossary entries and 9 guides at Wayfinder. The local model performs at a high-Sonnet to low-Opus tier for most execution tasks. It handles the 50-60% of SEO work that is data crunching and structured execution well.

What you need

Running a 122B parameter large language model requires serious hardware. A dedicated Grace Blackwell GB10 or similar GPU-heavy machine running Linux is standard. It is a lab, not an appliance. You need comfort with command-line tools, model configuration, and some willingness to tinker. It is not for everyone, but the ROI is compelling if you have the technical appetite and volume to justify it.

Matching Models to Tasks — A Practical Framework

A practical framework helps you avoid over-relying on a single tool or under-utilising specific capabilities.

Frontier cloud models (Claude, GPT-4o)

Use these for complex reasoning, creative strategy, production code, and anything where the quality of thinking is the priority.

Best for: Site audits and analysis, content strategy, competitive research, product development, code that goes to production.
Limitation: Cost scales with volume.

Large local models (Qwen 3.5 122B, Llama 3.x 70B+)

Use these for execution from detailed briefs, data analysis, batch processing, template-based content generation, and research synthesis. They are smart enough to produce good output from good instructions but are less ideal for open-ended creative work. Understanding how chunking and retrieval-augmented generation work helps you write better briefs for these models.

Best for: The structured execution half of SEO work.
Limitation: Requires hardware investment and technical maintenance.

Small specialised models (NemoTron Nano, DeepSeek Coder, Gemma)

Use these for narrow specific tasks where speed or cost matters more than quality. A small generalised model will be mediocre at everything; the key is to match the model to the task.

Best for: Code generation (DeepSeek Coder), fast basic tasks (NemoTron Nano).
Limitation: Do not expect a 7B model to do what a 122B model does.

What doesn't work

Using a small general-purpose model for complex analysis results in poor output. Using a frontier model for batch mechanical work is expensive and unnecessary. Using any model without understanding its specific strengths creates bottlenecks.

What We'd Recommend

The right setup depends on your volume, technical appetite, and budget.

Solo SEO practitioner / small team

Claude Pro (£90/month): Your primary working tool for strategy and analysis.
ChatGPT: For quick validation and second opinions.
Screaming Frog + manual testing: For technical audits.
Total: ~£100/month.
Consider our technical AEO checklist to ensure your technical foundations are sound before scaling content.

Growing team / agency

Claude: For strategy and complex work.
API access (Claude or OpenAI): For automated workflows.
Local model: Consider this if batch processing volume justifies the hardware investment.
Compass: For AI-specific navigation testing.
Total: £200-500/month + optional hardware investment.

Technical team willing to invest

Claude: For frontier reasoning.
Local 122B model: For batch execution (£3-4k one-time).
Self-built tooling: For prompt tracking and automated workflows.
Full Compass + Screaming Frog stack: For auditing.
Total: £3-4k upfront + ~£100/month ongoing.

However you build your AI toolkit, technical accessibility is the foundation. Compass tests how AI agents navigate your site — the one thing no model can tell you from a prompt. For more on evaluating your full AEO tool stack, see our landscape comparison.