1. What this calculator does
Model selection is an architecture decision, not a leaderboard decision. This calculator maps workload constraints to model classes and makes trade-offs explicit across cost, latency, privacy, accuracy, context window, and deployment control.
2. When to use it
- When comparing workload types: chatbot, RAG, NL-to-SQL, code generation, agentic workflows, and summarization pipelines.
- When teams need a decision matrix for latency vs cost vs accuracy vs privacy constraints.
- Before committing to one model family for enterprise deployment and governance reviews.
3. Inputs explained
- Workload shape: real-time chat, retrieval-heavy RAG, structured SQL generation, coding, multi-step agents, or batch summarization.
- Latency target, budget envelope, and quality threshold expected by business stakeholders.
- Privacy and residency requirements that can eliminate hosted frontier options early.
- Context-window needs and deployment controls for enterprise architecture standards.
4. Formula / decision logic
- Decision matrix: score candidate model classes across latency, cost, accuracy, privacy, and context window fit.
- Use small models for routing, classification, and low-risk extraction where speed and cost dominate.
- Use medium models for balanced production flows that need quality with tighter latency and budget controls.
- Use frontier models for high-ambiguity reasoning, policy-sensitive drafting, and hard long-context tasks.
- Use local/self-hosted models when privacy, sovereignty, or deterministic enterprise controls are hard constraints.
5. Example scenario
Example: enterprise RAG assistant. A knowledge assistant retrieves policy and product documentation, then routes short factual queries to a medium model while reserving frontier models for complex multi-document reasoning and ambiguous escalation cases.
6. Architecture implications
- Example: batch document summarization. For nightly summarization queues, medium or small models often outperform frontier models on cost-per-document while still meeting quality thresholds.
- Example: customer-support agent. Route intent detection and retrieval filtering to cheaper models; reserve stronger models for policy interpretation, exception handling, and human-handoff drafting.
- Model routing policy should be treated as architecture code with explicit SLO and governance checks.
- Selection rationale should be auditable for procurement, risk, and compliance review boards.
7. Common mistakes
- Choosing solely by benchmark rank without production latency and failure-rate testing.
- Ignoring model-routing patterns and overpaying for low-complexity tasks.
- Underestimating prompt and context overhead when projecting total token spend.
- Skipping red-team and governance checks for high-impact decision workflows.
9. FAQ
Should we choose one model for every workflow?
Usually no. Most enterprise stacks benefit from model routing: small/fast models for classification and extraction, stronger models for high-ambiguity reasoning, and specialized models for coding or multilingual tasks.
What matters more: benchmark score or production latency?
For production systems, latency and reliability often dominate after a baseline quality threshold is met. Optimize for end-to-end task success under real traffic, not leaderboard metrics alone.
When should we fine-tune instead of prompt-engineer?
Fine-tuning is justified when prompt-only approaches cannot consistently meet quality targets, and you have enough stable labeled data plus governance controls for retraining and drift monitoring.
