Model Tiering by Cost Per Task
socialwithaayan · x · 2026-07-10
The author categorizes models into four tiers based on cost per task to discuss selection strategies, rather than just looking at hype or single benchmarks.
- TIER 1: Cheap and fast, suitable for high-frequency tasks like classification, labeling, and routing. The text notes that Claude Haiku 4.5, GPT-5.6 Luna, and Gemini 3.1 Flash-Lite each have advantages in speed, cost-effectiveness, and lowest price, respectively.
- TIER 2: Daily default, for drafting, code reviews, and everyday agents. The author considers Claude Sonnet 5, GPT-5.6 Terra, and Gemini 3.1 Pro as representative of this tier.
- TIER 3: High-difficulty reasoning, for multi-hour agents and expensive failure scenarios like migrations/refactoring. The text mentions Claude Opus 4.8, GPT-5.6 Sol, and Gemini 3.1 Deep Think.
- TIER 4: Frontier capabilities, for SOTA coding or heavy reasoning where it must succeed on the first try. This corresponds to Claude Fable 5, GPT-5.6 Sol Ultra, and GPT-5.6 Sol Fast.
The core conclusion: cheaper models are winning on "cost per task", while frontier models win on capability, not necessarily cost-effectiveness. Models should be tiered by task difficulty to avoid spending frontier prices on low-value work.
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