Key takeaways
- Freeze representative tasks before testing.
- Measure cost per accepted outcome, not token price alone.
- Treat routing and exit costs as part of the architecture.
Begin with the workload
Write down the decisions the model must support, the inputs it may receive, the required output contract and the cost of a wrong answer. Split the workload into distinct task families instead of searching for one universal winner.
Test the complete path
Use production-shaped prompts, retrieval, tools, schemas and latency limits. Score acceptance, retries, human correction, failure detectability and cost per accepted outcome. A model that is cheaper per token can be more expensive after retries and review.
Design for change
Keep prompts, evaluations and tool contracts portable. Record model-specific assumptions and establish a fallback. The ability to switch can be more valuable than a small benchmark advantage that locks the system to one provider.
Make the decision reversible
Choose an initial deployment window, explicit success gates and a re-evaluation trigger. Keep a challenger test rather than continuously changing production based on release-day claims.
Primary sources
- NIST AI RMF: Generative Artificial Intelligence ProfileNational Institute of Standards and Technology
Limitations
This is an AccessAllGPT decision framework, not an original model evaluation. It does not replace domain-specific safety, legal or performance testing, and the appropriate task set and thresholds will vary by workload.
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