Token pricing is an incomplete metric. The real cost of AI is cost-per-task — how much it costs to complete a specific job. A 2026 Microsoft Research study found that the cheaper-listed model finished the same work at a higher cost in roughly a third of matchups.
Why Cost-Per-Task Matters
A model with lower token prices may require more calls, more tokens, or more retries to complete a task. A more expensive model per token that gets the answer right the first time is often cheaper overall. This is the price reversal phenomenon documented by Microsoft Research.
Measuring Effectively
Test models on a representative sample of your actual tasks. Measure total cost (tokens + retries + latency costs). Compare cost-per-task, not cost-per-token. Consider the hidden costs of verification and error correction.
Practical Recommendations
Budget models (Gemini Flash, GPT-5.6 Luna) are often most economical for simple, well-defined tasks. Premium models (Claude Opus 5, GPT-5.6 Terra) can be cheaper overall for complex tasks due to higher first-attempt accuracy. Test both on your workload before committing.
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