Business data & reporting
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Concept Stack 02 Stack 06 Stack 08
Model tiers (flagship, balanced, fast)
Verified Oct 17, 2026 Review by Apr 15, 2027 3 sources
Short answer
Vendors sell each model family in tiers, from a slow, expensive flagship to fast, cheap models for high-volume work. On current published input prices the gap is large: about 100× between OpenAI's GPT-6 Astra and GPT-6 Luna. Choosing the lightest tier that does the job is usually the biggest cost lever.
What it is
Each vendor publishes a ladder. Anthropic describes its fastest tier, Haiku, as built for high-volume, latency-sensitive tasks such as classification, extraction and routing. It describes its top tier as built for demanding reasoning and long agentic work. OpenAI frames its three steps as complex reasoning and coding (GPT-6 Astra), a balance of intelligence and cost (GPT-6.1 Sol) and cost-sensitive, high-volume work (GPT-6 Luna).
How it works
Higher tiers think longer and handle harder problems. Lower tiers answer faster and cost a fraction of the price. Many tasks don’t need the top tier: sorting, tagging, summarizing existing material, reformatting.
Christopher Penn’s rule of thumb is to pick the lightest model the task tolerates, especially when organizing existing material rather than creating something new. That’s a practitioner view. Test it on your own work.
Benefits
- Cost. Moving routine steps down a tier can cut their price by an order of magnitude or more on published rates (prices checked 2026-10-17).
- Speed. Smaller tiers respond faster, which matters in customer-facing flows.
- Routing. A cheap model can decide whether the expensive one needs to run at all (see cutting AI API costs).
Limits and risks
- Quality cliffs. Test the lower tier on real samples before switching a whole workflow (see evals).
- Tier names change with every release. Map tasks to tiers by role, not by version number.
Checked specs
Snapshot from official sources on Oct 17, 2026.
Best-fit stacks
Ops & admin automation
Hand repeat admin (receipts, logs, notices, handoffs) to workflows your agent can trigger.
Dev, web & IT
Design, code, host and ship sites and internal tools, with an AI editor doing most of the typing.
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 |
Levels come from each tool's recorded, sourced compatibility. How the levels work.
Related entries
Which AI model should I use for a business task? · QuestionHow do I cut AI API costs without hurting quality? · QuestionTokens · ConceptClaude (Anthropic) · ModelGPT models (OpenAI) · ModelI need a weekly business report without building it by hand · NeedInference · ConceptMulti-model aggregator · ConceptPhi models (Microsoft) · ModelReasoning models · ConceptWhen is a reasoning model worth the extra cost? · Question
Sources
- Models overview · Anthropicofficial accessed Oct 17, 2026
- Models · OpenAIofficial accessed Oct 17, 2026
- You Ask, I Answer: Multi Agent AI Orchestration Costs? · Christopher Penn, 2026-07-29Practitioner view accessed Oct 17, 2026
Practitioner views are restated in our words and labeled as views, not measured results. Product facts come from official sources dated above.
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