5.3 — Model Selection and Trade-offs

CCDV-F Domain 5 · Task 5.3

What You Need to Know

Family Order (Durable)

Haiku → Sonnet → Opus → Frontier. Quality, latency and cost are not independent. Moving up buys capability and spends money and time.

Family	Role
Haiku	Lowest latency and price; real-time, high-volume, sub-agent tasks
Sonnet	Best combination of speed and intelligence; code generation, data analysis, agentic tool use
Opus	Complex agentic coding and enterprise work; multihour autonomous agents
Frontier	Long-running agents, deep reasoning, long-horizon agentic tasks

Selection Is Constraint-Matching

Four criteria: Capabilities, Speed, Cost, Effort. Tuning effort is often a better lever than switching models. Effort comes first: sweep effort on the current model before a tier change.

Two starting strategies:

• Efficiency-first — start with the fastest tier; upgrade only for specific capability gaps
• Capability-first — start with the strongest starting point; optimise then consider lowering effort or downgrading

Compare on cost per completed task, not price per token. Sample the hardest tenth of your tasks — the ranking flips by workload.

The Eval Is the Gate

1. Create benchmark tests specific to your use case
2. Test with your actual prompts and data
3. Compare accuracy, response quality and edge cases
4. Weigh performance and cost trade-offs

Hold other variables still; change one thing at a time. Run each effort level in a separate session (changing effort mid-session starts the prompt cache over).

Lifecycle States

State	Guarantee
Active	Fully supported and recommended
Legacy	No longer receives updates; may be deprecated
Deprecated	Still functional; recommended replacement + retirement date
Retired	No longer available; requests fail

At least 60 days’ notice before retirement for publicly released models. Every model ID is a pinned snapshot.

A Model Change Is a Behavioural Change

Behaviour shifts (response length, instruction interpretation, tool-calling tendency). Interface changes break code (manual extended thinking, prefill, non-default temperature). Cost changes arrive even when nothing breaks. Gate any switch on your own eval; re-sweep effort; shadow on a slice of traffic before cutover.

Exam Traps

• Choosing the most capable model for quality, or the cheapest at volume, by default
• Comparing models on price per token, or migrating because of published benchmarks
• Treating a deprecated model as one whose requests already fail