# How to Choose the Right Model for Your Agent

**June 22, 2026**

You're building an agent. Which model do you use? The answer isn't "the best one" — it's "the right one for this subtask."

---

## The Model Landscape (Mid-2026)

| Tier | Models | Cost/1M tokens | Best For |
|------|--------|----------------|----------|
| **Frontier** | Claude Opus 4, GPT-5, Gemini Ultra 2.0 | $12-30 | Complex reasoning, code generation, safety-critical decisions |
| **Mid** | Claude Sonnet 4, GPT-5-mini, Gemini Pro 2.0 | $3-8 | Analysis, classification, summarization |
| **Cheap** | Claude Haiku 3.5, GPT-5-flash, Gemini Flash 2.0 | $0.15-0.50 | Bulk processing, extraction, simple tool calls |
| **Local** | Llama 4, DeepSeek Coder, Mistral Large 2 | Free (HW cost) | Private data, offline, latency-sensitive |

The price range from cheapest to most expensive is **200x**. Using the wrong tier for a task is like renting a dump truck to move a shoebox.

---

## The Decision Framework

```
Task complexity
      │
      ├─ Is it deterministic? (grep, parse, format, extract)
      │     → Use CHEAP model (Haiku/Flash)
      │
      ├─ Does it need reasoning? (analyze, explain, plan)
      │     → Use MID model (Sonnet/Pro)
      │
      ├─ Is it customer-facing? (report, email, summary)
      │     → Use FRONTIER model (Opus/GPT-5)
      │
      └─ Does it involve private data? (PII, IP, secrets)
            → Use LOCAL model (Llama/DeepSeek)
```

---

## Cascade Routing in Practice

```python
MODEL_TIERS = {
    "cheap": {
        "model": "claude-haiku-3.5-20260501",
        "cost_per_m_tokens": 0.15,
        "max_tokens": 4000,
    },
    "mid": {
        "model": "claude-sonnet-4-20260501",
        "cost_per_m_tokens": 3.00,
        "max_tokens": 8000,
    },
    "premium": {
        "model": "claude-opus-4-20260501",
        "cost_per_m_tokens": 15.00,
        "max_tokens": 16000,
    },
}

def route_to_tier(task_type, prompt):
    if task_type in ("extract", "parse", "format", "search", "classify"):
        tier = "cheap"
    elif task_type in ("analyze", "explain", "plan", "review"):
        tier = "mid"
    elif task_type in ("generate", "synthesize", "report", "decide"):
        tier = "premium"
    else:
        tier = "mid"  # safe default

    model = MODEL_TIERS[tier]
    response = call_llm(model["model"], prompt, model["max_tokens"])
    cost = (count_tokens(prompt) / 1_000_000) * model["cost_per_m_tokens"]

    return {"response": response, "tier": tier, "cost": cost}
```

---

## Model Selection Anti-Patterns

### "Just Use the Best Model"

The most expensive anti-pattern. Running every task through Opus costs 100x more than routing simple tasks to Haiku. For a production agent making 500 calls/day:
- All Opus: ~$75/day
- Cascaded: ~$8/day

**Savings: 89%**

### "Use the Cheapest Model Everywhere"

Saves money, loses quality. Cheap models hallucinate more, follow instructions less reliably, and produce worse output on complex tasks. A single bad output from a cheap model can cost more in debugging time than you saved in API fees.

### "One Model Per Agent"

This is acceptable for simple agents but misses optimization opportunities. Within a single agent session, you can route different subtasks to different models. The same agent can use Haiku for file discovery and Opus for synthesis.

---

## When Local Models Make Sense

Local models (Llama 4, DeepSeek) are not competitive with cloud APIs on quality. But they win on:

1. **Privacy** — Data never leaves your machine
2. **Latency** — No network calls (5ms vs 500ms)
3. **Cost at scale** — Free after hardware purchase
4. **Offline operation** — Works without internet

**Best use cases**: Code completion, local file analysis, private document review, development assistance.

**Worst use cases**: Complex reasoning, multi-step planning, tasks requiring up-to-date knowledge.

---

## The 80/20 Rule of Model Selection

80% of cost savings come from one change: **stop using frontier models for routine work**.

| Task Type | Current Model | Recommended Model | Savings |
|-----------|--------------|-------------------|---------|
| File discovery | Opus/Sonnet | Haiku/Flash | 95% |
| Data extraction | Opus/Sonnet | Haiku/Flash | 95% |
| Classification | Opus/Sonnet | Haiku/Flash | 95% |
| Analysis | Opus | Sonnet | 80% |
| Code review | Opus | Sonnet | 80% |
| Report generation | Opus | Opus (keep) | 0% |
| Complex reasoning | Opus | Opus (keep) | 0% |

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*This is adapted from Module 6: Economics of the [Agentic Engineering the Hard Way](/free-preview) course. Full course includes 65 lessons, 13 labs, and 20 skill kits.*