As a Chief AI Officer, one of your core skills should be model routing: knowing when a task deserves the extra reasoning power and cost of Opus versus when Sonnet is the smarter operational choice.
A useful rule is:
Sonnet = execution engine. Opus = thinking partner.
| Situation | Sonnet | Opus |
|---|---|---|
| Routine coding | ✅ | |
| Writing/editing | ✅ | |
| Summarizing documents | ✅ | |
| Data extraction / classification | ✅ | |
| Customer-support workflows | ✅ | |
| High-volume automation | ✅ | |
| Standard research | ✅ | |
| Complex strategic decision | ✅ | |
| Ambiguous problem with many tradeoffs | ✅ | |
| Deep architecture/design reasoning | ✅ | |
| Critiquing an important proposal | ✅ | |
| Finding flaws in another model’s work | ✅ | |
| High-stakes executive analysis | ✅ | |
| Problem where Sonnet keeps failing | ✅ |
The CAIO mindset
Don’t ask “Which model is better?”
Ask:
“What is the cheapest/fastest model that reliably clears the quality bar for this task?”
That usually means starting with Sonnet. Escalate to Opus when complexity, ambiguity, consequences, or repeated failure justify it.
There’s also a powerful enterprise pattern:
Sonnet → Opus → Sonnet
Sonnet does the bulk work → Opus reviews/reasons about the difficult part → Sonnet executes the resulting plan at scale.
That’s much more sophisticated than simply giving everyone access to the strongest model.
And the next level for you as CAIO is learning when to use GPT vs Sonnet vs Opus vs Gemini, because then you’re designing a model portfolio, not just choosing between two Claude models.
Instead of thinking “Which AI is best?”, think like an executive allocating different kinds of work to different specialists.
The CAIO Model-Routing Framework
| Need | Default choice | Why |
|---|---|---|
| Everyday knowledge work | GPT | Strong general-purpose reasoning + broad tool/workflow capability |
| Coding / implementation | GPT or Sonnet | Strong execution; test both on your actual codebase |
| Long-document analysis | Sonnet | Strong document-oriented knowledge work |
| Difficult strategic reasoning | Opus | Spend premium inference on problems where deeper reasoning has business value |
| Huge-context / Google-heavy workflows | Gemini | Particularly useful when Google’s ecosystem/context strengths matter |
| Images / multimodal workflows | GPT or Gemini | Choose based on the specific media workflow and integrations |
| High-volume repetitive work | Sonnet or a smaller/cheaper model | Optimize cost and latency rather than maximum intelligence |
| Mission-critical decision | Multiple models | Independent analysis reduces dependence on one model’s blind spots |
The important part is that model names and capabilities change quickly. As CAIO, you shouldn’t build policy around “Model X is always best.” Build it around task characteristics.
Your 5-question routing test
Before choosing a model, ask:
1. How hard is the reasoning?
Routine → standard model.
Deep, ambiguous, multi-step → reasoning/premium model.
2. How expensive is failure?
Internal draft → inexpensive model is fine.
Board recommendation, major contract analysis, architecture decision → spend more.
3. How much context is involved?
A short prompt is different from analyzing thousands of pages, a repository, or a massive dataset.
4. What tools/environment does the task require?
Sometimes the deciding factor isn’t intelligence. It’s whether the model can interact with your files, code, browser, enterprise systems, or other tools.
5. What’s the volume?
Doing something once and doing it 500,000 times/month are completely different economic decisions.
Then introduce escalation
This is where your AI architecture gets interesting.
Don’t send everything to the expensive model.
Tier 1 — Cheap/Fast
→ classify, extract, summarize, route
Tier 2 — Strong general model
→ produce the actual work
Tier 3 — Premium reasoning model
→ handle difficult exceptions
Tier 4 — Verification
→ another model, deterministic checks, or a human validates high-risk outputs
So instead of:
Employee → Opus
you eventually build:
Request → Router → Sonnet/GPT/Gemini → confidence/risk check → Opus when necessary → human when necessary
That can dramatically change the economics of enterprise AI.
One concept I especially want you to master
Think in terms of:
Cost × Latency × Quality × Risk
Imagine two models:
Model A: $0.08/task, 95% acceptable
Model B: $0.80/task, 98% acceptable
A beginner says:
“B is better.”
A CAIO asks:
“Is that extra 3 percentage points worth 10× the cost for this workflow?”
For payroll-related decisions, perhaps absolutely.
For generating 100,000 product-description drafts? Probably not.
And sometimes the smartest system is:
A handles 95% → B handles uncertain cases → humans handle the truly consequential exceptions.
That is AI systems thinking, rather than simply being good at prompting.
Where I’d take your training next
The next concept I’d teach you is how to build a model evaluation scorecard so that when someone says “Gemini is better than GPT” or “we should switch everything to Claude,” you can respond:
“Better at what? Show me our evals.”
That is the kind of question a strong Chief AI Officer should be able to ask.