SOPs & process documentation
Turn how your team actually works into SOPs people can follow and agents can use.
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Question Stack 01 Stack 04 Stack 07
Which AI model should I use for a business task?
Draft · pending editorial review Verified Oct 16, 2026 Review by Jan 14, 2027 5 sources
Short answer
Start from the task, not the leaderboard. For most text and code work, today's frontier models are close enough that your instructions, data and setup matter more than the vendor. Pick the ecosystem your team already uses and the lightest tier that passes your test. Switch only for a hard capability gap, such as image generation.
What it is
Teams lose weeks comparing models. This page sets out the order of decisions we recommend, and shows when the model choice actually matters.
How it works
- Define the output and a pass test. Collect five real examples and what “good” looks like.
- Start where your work lives. Google Workspace teams can start with Gemini, ChatGPT teams with GPT models, and document-heavy teams often try Claude.
- Use the lightest tier that passes (see model tiers).
- Fix instructions before switching vendors. Christopher Penn argues that poor output from a frontier model is usually a prompting or setup problem, not a model problem.
- Switch only for hard gaps. For example, Anthropic’s current models output text only, so image generation needs another model.
Benefits
- Faster decisions and less tool sprawl.
- Portable prompts. Work on instructions and data carries over if you change vendors later.
Limits and risks
- Edges are real but temporary. Vendors leapfrog each other often. Re-test quarterly rather than chasing every release.
- Practitioner view. “Models are interchangeable for text” is Penn’s informed opinion. Test it on your own tasks.
Best-fit stacks
Marketing content & social
Research, write, design and publish on-brand content, with the agent handling the first draft of everything.
Research & market intelligence
Competitor, market and search research with sources you can cite.
Agent compatibility
| Tool in this entry | How agents connect |
|---|---|
| Claude | Agent platform API/CLIBulk import |
| ChatGPT | Agent platform API/CLIBulk import |
| Gemini (Google) | Agent platform API/CLIBulk import |
| Grok | Agent platform API/CLI Bulk import? |
Levels come from each tool's recorded, sourced compatibility. How the levels work.
Related entries
Model tiers (flagship, balanced, fast) · ConceptAgent harness · ConceptClaude (Anthropic) · ModelGPT models (OpenAI) · ModelGemini models (Google) · ModelGrok models (xAI) · ModelAI assistants · ServiceEvals (AI evaluations) · ConceptHow do I choose an AI assistant for a small team? · QuestionHow do I cut AI API costs without hurting quality? · QuestionHow do I judge AI tool recommendations from creators and review sites? · QuestionHow do I know if AI output is good enough to use? · QuestionLarge language model (LLM) · ConceptMuse models (Meta) · ModelWhat is Gab AI? · QuestionWhen is a reasoning model worth the extra cost? · QuestionWhich AI platform should I use for what? · Question
Sources
- You Ask, I Answer: Evaluating LLMs the Wrong Way? · Christopher Penn, 2026-08-13Practitioner view accessed Oct 16, 2026
- You Ask, I Answer: Multi Agent AI Orchestration Costs? · Christopher Penn, 2026-07-29Practitioner view accessed Oct 16, 2026
- Models overview · Anthropicofficial accessed Oct 16, 2026
- Models · OpenAIofficial accessed Oct 16, 2026
- Gemini API models · Google, 2026-10-13official accessed Oct 16, 2026
Practitioner views are restated in our words and labeled as views, not measured results. Product facts come from official sources dated above.
To verify before publishing
- Re-check each recommendation against the model pages every 30 days
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