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Agent Uses Wrong Model for Task Type — Slow/Expensive Model for Simple Tasks

Symptom

• Every API call uses the most expensive model — costs 10x more than necessary
• Agent uses a fast/cheap model for complex multi-step planning — output quality suffers
• Classification task (3 categories) uses Opus — takes 8 seconds and costs $0.15 per call
• Complex code generation uses Haiku — produces incorrect, incomplete code
• Agent hardcodes model="claude-opus-4-6" for all calls — no differentiation by task type
• Routing logic doesn’t exist — model choice was made once at setup and never revisited

Root Cause

Most agents are built with a single model ID hardcoded for all tasks. But different tasks have very different requirements: simple classification, extraction, or yes/no decisions need fast and cheap models; complex reasoning, code generation, and multi-step planning need capable models. Without task-based model routing, the agent either over-spends on simple tasks or under-performs on complex ones. The fix is to classify tasks by complexity and route each call to the appropriate model tier.

Fix

Option 1: Task-type routing table — map task types to models

import anthropic
from enum import Enum
from dataclasses import dataclass

client = anthropic.Anthropic()

class TaskComplexity(Enum):
    TRIVIAL = "trivial"      # Classification, extraction, yes/no, formatting
    SIMPLE = "simple"        # Summarization, translation, short generation
    STANDARD = "standard"    # Multi-step reasoning, code generation, analysis
    COMPLEX = "complex"      # Architecture design, deep research, novel reasoning

@dataclass
class ModelConfig:
    model_id: str
    max_tokens: int
    description: str

# Model routing table — update when models change
MODEL_ROUTING: dict[TaskComplexity, ModelConfig] = {
    TaskComplexity.TRIVIAL: ModelConfig(
        model_id="claude-haiku-4-5-20251001",
        max_tokens=256,
        description="Fast classification and extraction"
    ),
    TaskComplexity.SIMPLE: ModelConfig(
        model_id="claude-haiku-4-5-20251001",
        max_tokens=1024,
        description="Summarization and short generation"
    ),
    TaskComplexity.STANDARD: ModelConfig(
        model_id="claude-sonnet-4-6",
        max_tokens=4096,
        description="Multi-step reasoning and code generation"
    ),
    TaskComplexity.COMPLEX: ModelConfig(
        model_id="claude-opus-4-6",
        max_tokens=8192,
        description="Deep analysis and architectural decisions"
    ),
}

# Explicit task type assignments — no guessing
TASK_TYPES: dict[str, TaskComplexity] = {
    # Trivial — always use Haiku
    "classify_sentiment": TaskComplexity.TRIVIAL,
    "extract_entities": TaskComplexity.TRIVIAL,
    "is_spam": TaskComplexity.TRIVIAL,
    "categorize_topic": TaskComplexity.TRIVIAL,
    "extract_date": TaskComplexity.TRIVIAL,
    "yes_no_question": TaskComplexity.TRIVIAL,
    "format_output": TaskComplexity.TRIVIAL,

    # Simple — Haiku with more tokens
    "summarize_text": TaskComplexity.SIMPLE,
    "translate": TaskComplexity.SIMPLE,
    "rewrite_tone": TaskComplexity.SIMPLE,
    "extract_key_points": TaskComplexity.SIMPLE,

    # Standard — Sonnet
    "generate_code": TaskComplexity.STANDARD,
    "debug_code": TaskComplexity.STANDARD,
    "explain_concept": TaskComplexity.STANDARD,
    "write_tests": TaskComplexity.STANDARD,
    "analyze_data": TaskComplexity.STANDARD,
    "plan_task": TaskComplexity.STANDARD,

    # Complex — Opus
    "architect_system": TaskComplexity.COMPLEX,
    "deep_research": TaskComplexity.COMPLEX,
    "security_audit": TaskComplexity.COMPLEX,
    "novel_algorithm": TaskComplexity.COMPLEX,
}

def call_with_routing(
    task_type: str,
    prompt: str,
    system: str = None,
    override_complexity: TaskComplexity = None
) -> str:
    """
    Call Claude with the right model for this task type.
    Use override_complexity for one-off adjustments.
    """
    complexity = override_complexity or TASK_TYPES.get(task_type, TaskComplexity.STANDARD)
    config = MODEL_ROUTING[complexity]

    print(f"Task '{task_type}' → {config.model_id} (max_tokens={config.max_tokens})")

    messages = [{"role": "user", "content": prompt}]
    kwargs = {"model": config.model_id, "max_tokens": config.max_tokens, "messages": messages}
    if system:
        kwargs["system"] = system

    response = client.messages.create(**kwargs)
    return response.content[0].text

# Usage:
sentiment = call_with_routing("classify_sentiment", "Rate this review as positive/negative: 'Great product!'")
# → Uses Haiku (~0.001 cost, ~0.3s)

architecture = call_with_routing("architect_system", "Design a distributed event processing system for 10M events/day")
# → Uses Opus (full capability, appropriate cost)

Option 2: Automatic complexity inference — classify before routing

import anthropic
from typing import Optional

client = anthropic.Anthropic()

COMPLEXITY_CLASSIFIER_PROMPT = """Classify the complexity of this AI task into one of four levels:

TRIVIAL: Simple extraction, yes/no decisions, classification into fixed categories, format conversion
SIMPLE: Short generation, summarization, translation, basic rewriting
STANDARD: Code generation, multi-step analysis, debugging, structured planning
COMPLEX: Novel reasoning, architecture design, deep research, tasks requiring sustained creativity

Task to classify: {task}

Reply with exactly one word: TRIVIAL, SIMPLE, STANDARD, or COMPLEX"""

_complexity_cache: dict[str, TaskComplexity] = {}

def infer_task_complexity(task_description: str) -> TaskComplexity:
    """
    Use a cheap model to classify task complexity before routing.
    Results are cached to avoid repeated classification calls.
    """
    cache_key = task_description[:200]
    if cache_key in _complexity_cache:
        return _complexity_cache[cache_key]

    response = client.messages.create(
        model="claude-haiku-4-5-20251001",  # Always use cheapest for meta-classification
        max_tokens=20,
        messages=[{
            "role": "user",
            "content": COMPLEXITY_CLASSIFIER_PROMPT.format(task=task_description[:500])
        }]
    )

    label = response.content[0].text.strip().upper()
    complexity_map = {
        "TRIVIAL": TaskComplexity.TRIVIAL,
        "SIMPLE": TaskComplexity.SIMPLE,
        "STANDARD": TaskComplexity.STANDARD,
        "COMPLEX": TaskComplexity.COMPLEX,
    }
    complexity = complexity_map.get(label, TaskComplexity.STANDARD)
    _complexity_cache[cache_key] = complexity

    print(f"Task complexity: {label} → {MODEL_ROUTING[complexity].model_id}")
    return complexity

def call_with_auto_routing(
    task: str,
    prompt: str,
    system: str = None
) -> str:
    """Route automatically based on inferred task complexity"""
    complexity = infer_task_complexity(task)
    config = MODEL_ROUTING[complexity]

    messages = [{"role": "user", "content": prompt}]
    kwargs = {"model": config.model_id, "max_tokens": config.max_tokens, "messages": messages}
    if system:
        kwargs["system"] = system

    response = client.messages.create(**kwargs)
    return response.content[0].text

Option 3: Cost-aware routing with budget tracking

import time
from dataclasses import dataclass, field
from typing import Optional

# Approximate costs per million tokens (update as pricing changes)
MODEL_COSTS = {
    "claude-haiku-4-5-20251001": {"input": 0.80, "output": 4.00},
    "claude-sonnet-4-6":         {"input": 3.00, "output": 15.00},
    "claude-opus-4-6":           {"input": 15.00, "output": 75.00},
}

@dataclass
class UsageTracker:
    """Track costs by model and task type"""
    _calls: list[dict] = field(default_factory=list)

    def record(self, model: str, task_type: str, input_tokens: int, output_tokens: int):
        costs = MODEL_COSTS.get(model, {"input": 0, "output": 0})
        cost = (input_tokens * costs["input"] + output_tokens * costs["output"]) / 1_000_000
        self._calls.append({
            "model": model,
            "task_type": task_type,
            "input_tokens": input_tokens,
            "output_tokens": output_tokens,
            "cost_usd": cost,
            "timestamp": time.time()
        })

    def report(self) -> dict:
        if not self._calls:
            return {}
        total_cost = sum(c["cost_usd"] for c in self._calls)
        by_model = {}
        for call in self._calls:
            m = call["model"]
            by_model.setdefault(m, {"calls": 0, "cost": 0})
            by_model[m]["calls"] += 1
            by_model[m]["cost"] += call["cost_usd"]

        return {
            "total_calls": len(self._calls),
            "total_cost_usd": round(total_cost, 4),
            "by_model": {m: {**v, "cost": round(v["cost"], 4)} for m, v in by_model.items()},
            "potential_savings": self._calculate_savings()
        }

    def _calculate_savings(self) -> dict:
        """Estimate savings if simple tasks used cheaper models"""
        savings = 0
        for call in self._calls:
            if call["model"] == "claude-opus-4-6":
                haiku_cost = (
                    call["input_tokens"] * 0.80 + call["output_tokens"] * 4.00
                ) / 1_000_000
                opus_cost = call["cost_usd"]
                savings += (opus_cost - haiku_cost)
        return {"if_haiku_for_simple": round(savings, 4)}

tracker = UsageTracker()

def call_with_cost_tracking(
    task_type: str,
    prompt: str,
    system: str = None
) -> tuple[str, dict]:
    """Call with routing and cost tracking"""
    complexity = TASK_TYPES.get(task_type, TaskComplexity.STANDARD)
    config = MODEL_ROUTING[complexity]

    messages = [{"role": "user", "content": prompt}]
    kwargs = {"model": config.model_id, "max_tokens": config.max_tokens, "messages": messages}
    if system:
        kwargs["system"] = system

    response = client.messages.create(**kwargs)
    text = response.content[0].text

    tracker.record(
        model=config.model_id,
        task_type=task_type,
        input_tokens=response.usage.input_tokens,
        output_tokens=response.usage.output_tokens
    )

    return text, tracker.report()

Option 4: Multi-tier agent pipeline — route by stage

import anthropic

client = anthropic.Anthropic()

class TieredAgentPipeline:
    """
    Multi-stage pipeline where each stage uses the right model tier.
    Stage 1 (Planning): Opus — needs deep reasoning
    Stage 2 (Execution): Sonnet — needs reliable code/text generation
    Stage 3 (Verification): Haiku — needs fast yes/no checks
    Stage 4 (Formatting): Haiku — pure transformation
    """

    def plan(self, task: str, context: str = "") -> str:
        """Use Opus to deeply understand the task and create a plan"""
        response = client.messages.create(
            model="claude-opus-4-6",
            max_tokens=2048,
            system="You are a senior architect. Create a detailed, step-by-step execution plan.",
            messages=[{"role": "user", "content": f"Task: {task}\nContext: {context}\n\nCreate a numbered execution plan."}]
        )
        return response.content[0].text

    def execute_step(self, step: str, plan_context: str) -> str:
        """Use Sonnet to execute each step from the plan"""
        response = client.messages.create(
            model="claude-sonnet-4-6",
            max_tokens=4096,
            system="Execute the given step precisely. Output only the result of this step.",
            messages=[{
                "role": "user",
                "content": f"Plan context:\n{plan_context}\n\nExecute this step:\n{step}"
            }]
        )
        return response.content[0].text

    def verify(self, output: str, criterion: str) -> bool:
        """Use Haiku for fast yes/no verification"""
        response = client.messages.create(
            model="claude-haiku-4-5-20251001",
            max_tokens=10,
            messages=[{
                "role": "user",
                "content": f"Does this output satisfy the criterion? Reply YES or NO only.\n\nOutput: {output[:1000]}\n\nCriterion: {criterion}"
            }]
        )
        return "YES" in response.content[0].text.upper()

    def format_output(self, raw_output: str, output_format: str) -> str:
        """Use Haiku for pure formatting transformation"""
        response = client.messages.create(
            model="claude-haiku-4-5-20251001",
            max_tokens=2048,
            messages=[{
                "role": "user",
                "content": f"Reformat the following into {output_format}:\n\n{raw_output}"
            }]
        )
        return response.content[0].text

    def run(self, task: str, output_format: str = "markdown") -> str:
        """Run the full tiered pipeline"""
        print("Stage 1: Planning (Opus)")
        plan = self.plan(task)

        steps = [line for line in plan.split("\n") if line.strip() and line[0].isdigit()]
        results = []

        print(f"Stage 2: Executing {len(steps)} steps (Sonnet)")
        for step in steps:
            result = self.execute_step(step, plan)
            results.append(result)

        combined = "\n\n".join(results)

        print("Stage 3: Verifying (Haiku)")
        is_complete = self.verify(combined, f"The task '{task}' is fully addressed")
        if not is_complete:
            print("Output incomplete — re-executing with full plan context")
            combined = self.execute_step("Complete the full task", f"Task: {task}\nPlan: {plan}")

        print("Stage 4: Formatting (Haiku)")
        return self.format_output(combined, output_format)

pipeline = TieredAgentPipeline()

Option 5: Environment-based model config — different models per environment

import os
from dataclasses import dataclass

@dataclass
class EnvironmentModelConfig:
    """
    Different model choices per environment.
    Development: cheap/fast for iteration speed
    Staging: same as production
    Production: optimized for quality/cost balance
    """
    trivial: str
    simple: str
    standard: str
    complex: str

MODEL_CONFIGS = {
    "development": EnvironmentModelConfig(
        trivial="claude-haiku-4-5-20251001",
        simple="claude-haiku-4-5-20251001",
        standard="claude-haiku-4-5-20251001",  # Use Haiku everywhere in dev for speed/cost
        complex="claude-sonnet-4-6"             # Only use Sonnet for complex in dev
    ),
    "staging": EnvironmentModelConfig(
        trivial="claude-haiku-4-5-20251001",
        simple="claude-haiku-4-5-20251001",
        standard="claude-sonnet-4-6",
        complex="claude-opus-4-6"
    ),
    "production": EnvironmentModelConfig(
        trivial="claude-haiku-4-5-20251001",
        simple="claude-haiku-4-5-20251001",
        standard="claude-sonnet-4-6",
        complex="claude-opus-4-6"
    ),
}

def get_model_for_env(complexity: TaskComplexity) -> str:
    """Get the right model for this complexity level in the current environment"""
    env = os.getenv("AGENT_ENV", "development")
    config = MODEL_CONFIGS.get(env, MODEL_CONFIGS["development"])

    return {
        TaskComplexity.TRIVIAL: config.trivial,
        TaskComplexity.SIMPLE: config.simple,
        TaskComplexity.STANDARD: config.standard,
        TaskComplexity.COMPLEX: config.complex,
    }[complexity]

Option 6: Routing audit — identify misrouted calls in logs

from dataclasses import dataclass
from typing import Optional

@dataclass
class RoutingAuditEntry:
    task_type: str
    model_used: str
    input_tokens: int
    output_tokens: int
    cost_usd: float
    recommended_model: Optional[str] = None
    potential_savings: float = 0.0

class RoutingAuditor:
    """
    Audit model routing decisions — find over/under-spending patterns.
    Run on production logs weekly to tune the routing table.
    """

    COMPLEXITY_SIGNALS = {
        # Signals that suggest TRIVIAL complexity (Haiku is sufficient)
        "trivial": [
            "classify", "extract", "yes or no", "true or false",
            "sentiment", "category", "label", "tag", "format as"
        ],
        # Signals that suggest COMPLEX (Opus is warranted)
        "complex": [
            "architect", "design system", "security audit", "novel approach",
            "comprehensive analysis", "detailed research", "complex algorithm"
        ]
    }

    def audit_call(
        self,
        task_description: str,
        model_used: str,
        input_tokens: int,
        output_tokens: int
    ) -> RoutingAuditEntry:
        costs = MODEL_COSTS.get(model_used, {"input": 0, "output": 0})
        actual_cost = (input_tokens * costs["input"] + output_tokens * costs["output"]) / 1_000_000

        # Check if this looks misrouted
        task_lower = task_description.lower()
        is_trivial_task = any(s in task_lower for s in self.COMPLEXITY_SIGNALS["trivial"])
        is_complex_task = any(s in task_lower for s in self.COMPLEXITY_SIGNALS["complex"])

        recommended = None
        savings = 0.0

        if is_trivial_task and model_used != "claude-haiku-4-5-20251001":
            recommended = "claude-haiku-4-5-20251001"
            haiku_cost = (input_tokens * 0.80 + output_tokens * 4.00) / 1_000_000
            savings = actual_cost - haiku_cost

        elif is_complex_task and model_used == "claude-haiku-4-5-20251001":
            recommended = "claude-opus-4-6"
            # No savings — it costs more, but quality matters here

        return RoutingAuditEntry(
            task_type=task_description[:80],
            model_used=model_used,
            input_tokens=input_tokens,
            output_tokens=output_tokens,
            cost_usd=actual_cost,
            recommended_model=recommended,
            potential_savings=savings
        )

    def generate_report(self, entries: list[RoutingAuditEntry]) -> dict:
        misrouted = [e for e in entries if e.recommended_model]
        total_savings = sum(e.potential_savings for e in misrouted)
        return {
            "total_calls": len(entries),
            "misrouted_calls": len(misrouted),
            "potential_savings_usd": round(total_savings, 4),
            "misrouted_examples": [
                f"'{e.task_type}' used {e.model_used} — should use {e.recommended_model} (save ${e.potential_savings:.4f})"
                for e in misrouted[:5]
            ]
        }

auditor = RoutingAuditor()

Model Selection by Task Type

Task Category	Examples	Recommended Model	Rationale
Binary decisions	Spam detection, sentiment, yes/no	Haiku	Single-token answer
Extraction	Entity extraction, date parsing, field extraction	Haiku	Pattern matching
Short generation	Titles, labels, tags, short descriptions	Haiku	Low complexity
Summarization	Document summary, key points	Haiku	Compression task
Code generation	Functions, classes, modules	Sonnet	Reliable reasoning
Debugging	Root cause analysis, fix suggestion	Sonnet	Multi-step logic
Planning	Task breakdown, sequence design	Sonnet	Structured output
Architecture	System design, security audit	Opus	Novel reasoning
Deep research	Comprehensive analysis, synthesis	Opus	Sustained complexity

Expected Token Savings

All tasks on Opus: 100% of potential cost spent Routed by task type: ~70% cost reduction (most tasks are trivial/simple) Example: 1M calls/day → 95% Haiku + 4% Sonnet + 1% Opus = ~85% cost savings vs all-Opus

Environment

• Any production agent with mixed task types; model routing is the highest-leverage cost optimization available — a single routing table can reduce API costs by 50-90% without any loss of output quality for complex tasks
• Source: direct experience; hardcoded model selection is universal in early-stage agents and universally replaced with routing tables within the first month of production operation

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