Multi-Model Evaluation: Choosing the Right LLM for Each Task

The Multi-Model Reality

Three months ago, you standardized on GPT-4 for everything. It was simpler: one API, one set of prompts, predictable costs. But your bill is $50K/month and growing. Meanwhile, GPT-3.5 costs 95% less, Claude 3.5 Sonnet is faster, and open-source Llama 3 70B is free to run.

The question isn't "which model is best?"—it's "which model is best for this specific task?" The answer changes based on:

• Task complexity (simple classification vs. complex reasoning)
• Quality requirements (mission-critical vs. good enough)
• Latency constraints (real-time chat vs. batch processing)
• Cost budgets (high-volume vs. occasional use)
• Privacy needs (API vs. self-hosted)

Multi-model strategies treat models as specialized tools, not universal solutions. You evaluate each model against each task, then route workloads intelligently. The result: better quality, lower cost, and more resilience.

The Model Landscape: Strengths and Weaknesses

Frontier Models (GPT-4, Claude 3 Opus, Gemini Ultra)

Strengths:

• Complex reasoning and multi-step tasks
• Following intricate instructions
• Creative writing and nuanced content
• Strong performance out-of-the-box with minimal prompting

Weaknesses:

• Expensive ($0.03-$0.06 per 1K tokens)
• Slower (2-5 seconds latency)
• Overkill for simple tasks

Best for:

• High-stakes decisions (legal analysis, medical information)
• Complex content generation (long-form articles, code with architecture)
• Tasks where quality >>> cost

Mid-Tier Models (GPT-3.5, Claude 3 Sonnet, Gemini Pro)

Strengths:

• Good balance of cost and quality
• Faster than frontier models (1-3 seconds)
• Capable of most common tasks with good prompting

Weaknesses:

• Struggle with very complex reasoning
• May need more prompt engineering than GPT-4
• Less consistent on edge cases

Best for:

• Customer support (straightforward Q&A)
• Content summarization
• Classification with some nuance
• High-volume applications where cost matters

Specialized Models (Embedding models, code-specific, etc.)

Strengths:

• Optimized for specific tasks
• Very cheap or free
• Fast inference

Weaknesses:

• Limited to narrow use cases
• Not general-purpose

Best for:

• Embeddings for semantic search (text-embedding-3-small)
• Code completion (CodeLlama, StarCoder)
• Sentiment analysis (fine-tuned BERT models)

Open-Source Models (Llama 3, Mistral, Qwen)

Strengths:

• Free to run (pay for compute only)
• Full control over deployment
• Privacy (nothing leaves your infrastructure)
• Can fine-tune for specific domains

Weaknesses:

• Require infrastructure setup
• Generally lower quality than frontier models (but gap is closing)
• Need more prompt engineering

Best for:

• High-volume workloads where API costs are prohibitive
• Sensitive data that can't be sent to third parties
• Custom fine-tuning for domain-specific tasks

Systematic Multi-Model Evaluation

Step 1: Define Your Task Matrix

List all LLM tasks in your application:

Task	Volume	Latency Target	Quality Requirement	Current Model	Current Cost
Customer support Q&A	10K/day	<2s	High	GPT-4	$1,200/day
Product description generation	500/day	<10s	Medium	GPT-4	$30/day
Email summarization	50K/day	<1s	Medium	GPT-4	$3,000/day
Sentiment classification	100K/day	<500ms	Low	GPT-4	$6,000/day

Total monthly cost: $310,000

Step 2: Build Task-Specific Eval Sets

For each task, create a benchmark:

• 100-200 representative examples
• Covering common cases, edge cases, and adversarial inputs
• With ground truth or quality annotations

Example: Customer support Q&A eval set

{
  "task": "customer-support-qa",
  "examples": [
    {
      "input": "How do I reset my password?",
      "category": "common",
      "difficulty": "easy",
      "expected_elements": ["check email", "click reset link", "check spam"]
    },
    {
      "input": "I was charged twice for the same order, what should I do?",
      "category": "billing",
      "difficulty": "medium",
      "expected_elements": ["verify charges", "contact support", "provide order number"]
    },
    // ... 98 more examples
  ]
}

Step 3: Evaluate All Candidate Models

Test multiple models on each task:

models_to_test = [
    "gpt-4-turbo",
    "gpt-3.5-turbo",
    "claude-3-opus",
    "claude-3-sonnet",
    "claude-3-haiku",
    "gemini-pro",
    "llama-3-70b",
    "mistral-large"
]

results = {}
for model in models_to_test:
    results[model] = evaluate(
        prompt=CUSTOMER_SUPPORT_PROMPT,
        model=model,
        dataset="customer-support-qa",
        metrics=["accuracy", "completeness", "tone", "safety"]
    )

Collect:

• Quality metrics (accuracy, completeness, tone)
• Performance metrics (latency P50/P95, throughput)
• Cost per query

Step 4: Compare Model Performance

Example results: Customer support Q&A

Model	Accuracy	Completeness	Tone	Safety	P95 Latency	Cost/Query	Cost-Quality Ratio
GPT-4 Turbo	89%	87%	8.2/10	100%	3.2s	$0.012	$0.0135
GPT-3.5 Turbo	78%	74%	7.1/10	98%	1.8s	$0.0006	$0.00077
Claude 3 Opus	91%	89%	8.5/10	100%	2.9s	$0.015	$0.0165
Claude 3 Sonnet	85%	83%	8.0/10	100%	1.5s	$0.003	$0.0035
Claude 3 Haiku	76%	72%	7.3/10	99%	0.9s	$0.00025	$0.00033
Gemini Pro	82%	80%	7.6/10	99%	2.1s	$0.00025	$0.00030
Llama 3 70B	73%	69%	6.8/10	97%	2.5s	$0.0002*	$0.00027

*Assuming self-hosted compute costs

Analysis:

• Best quality: Claude 3 Opus, but expensive
• Best cost-quality ratio: Gemini Pro or Claude 3 Haiku
• Fastest: Claude 3 Haiku
• Cheapest: Llama 3 70B (self-hosted) or Gemini Pro/Haiku

Decision depends on priorities:

• If quality is paramount: Claude 3 Opus
• If cost matters: Switch from GPT-4 to Gemini Pro (save 98%, lose 7% accuracy)
• If speed matters: Claude 3 Haiku

Step 5: Repeat for All Tasks

Do this for every task. You'll often find:

• Simple tasks (sentiment analysis) work fine with cheap models
• Complex tasks (legal analysis) need frontier models
• Mid-tier tasks have good cost-quality tradeoffs with mid-tier models

Example: Email summarization

Model	Quality	Cost/Query	Daily Cost (50K queries)
GPT-4	92%	$0.06	$3,000
GPT-3.5	87%	$0.003	$150
Claude 3 Haiku	88%	$0.00025	$12.50

Decision: Claude 3 Haiku gives 88% quality (only 4% drop from GPT-4) at 99.6% cost savings. Switch and save $2,987/day.

Building a Model Router

Once you've evaluated models per task, build a routing layer that sends each request to the optimal model.

Simple Static Routing

Map tasks to models in config:

model_routing:
  customer-support-qa:
    model: claude-3-sonnet
    fallback: gpt-4-turbo
  
  email-summarization:
    model: claude-3-haiku
    fallback: gpt-3.5-turbo
  
  sentiment-classification:
    model: custom-bert-fine-tuned
    fallback: claude-3-haiku
  
  legal-document-analysis:
    model: gpt-4-turbo
    fallback: claude-3-opus

Implementation:

class ModelRouter:
    def __init__(self, routing_config):
        self.routes = routing_config
    
    async def route(self, task: str, input: str) -> str:
        route = self.routes.get(task)
        if not route:
            raise ValueError(f"No route configured for task: {task}")
        
        try:
            # Try primary model
            response = await llm_call(
                prompt=PROMPTS[task],
                input=input,
                model=route['model'],
                timeout=5.0
            )
            return response
        except Exception as e:
            # Fall back to backup model
            logger.warning(f"Primary model failed, falling back: {e}")
            response = await llm_call(
                prompt=PROMPTS[task],
                input=input,
                model=route['fallback'],
                timeout=10.0
            )
            return response

# Usage
router = ModelRouter(routing_config)
answer = await router.route("customer-support-qa", user_question)

Dynamic Routing Based on Input Complexity

Some tasks vary in difficulty. Route simple cases to cheap models, hard cases to expensive ones.

Example: Customer support

• Simple questions ("How do I reset password?") → Claude 3 Haiku
• Complex questions ("I was charged incorrectly and my account is suspended") → GPT-4

Complexity classifier:

async def classify_complexity(question: str) -> str:
    """
    Classify question complexity using a small, fast model.
    """
    classifier_prompt = f"""
    Classify this customer question as SIMPLE, MEDIUM, or COMPLEX:
    
    SIMPLE: One-step answer, common question
    MEDIUM: Requires some explanation or multiple steps
    COMPLEX: Multi-part issue, requires judgment or policy interpretation
    
    Question: {question}
    
    Classification:
    """
    
    result = await llm_call(
        classifier_prompt,
        model="claude-3-haiku",  # Fast, cheap classifier
        max_tokens=10
    )
    
    return result.strip().upper()

async def route_by_complexity(question: str) -> str:
    complexity = await classify_complexity(question)
    
    if complexity == "SIMPLE":
        model = "claude-3-haiku"
    elif complexity == "MEDIUM":
        model = "claude-3-sonnet"
    else:
        model = "gpt-4-turbo"
    
    return await llm_call(CUSTOMER_SUPPORT_PROMPT, question, model=model)

Cost savings: If 60% of questions are SIMPLE, 30% MEDIUM, 10% COMPLEX:

• Before (all GPT-4): $0.012 × 10K = $120/day
• After (routed): (0.6 × $0.00025) + (0.3 × $0.003) + (0.1 × $0.012) = $0.00285/query × 10K = $28.50/day
• Savings: 76%

Hybrid Approaches: Cascade and Retry

Start with a cheap model. If it fails or produces low-confidence output, retry with a better model.

Example: Content moderation

async def moderate_content(text: str) -> dict:
    """
    Cascade: try fast model first, escalate if uncertain.
    """
    # Try cheap classifier
    fast_result = await llm_call(
        prompt=MODERATION_PROMPT,
        input=text,
        model="claude-3-haiku",
        response_format="json"
    )
    
    # If high confidence (very safe or very unsafe), trust it
    if fast_result['confidence'] > 0.9:
        return fast_result
    
    # If uncertain, escalate to better model
    detailed_result = await llm_call(
        prompt=DETAILED_MODERATION_PROMPT,
        input=text,
        model="gpt-4-turbo",
        response_format="json"
    )
    
    return detailed_result

Cost profile:

• 80% of content is clearly safe/unsafe: $0.00025/query
• 20% needs escalation: $0.00025 + $0.012 = $0.01225/query
• Average: (0.8 × $0.00025) + (0.2 × $0.01225) = $0.00265/query
• vs. always using GPT-4: $0.012/query (78% savings)

Model-Specific Prompt Optimization

Different models have different strengths. Prompts optimized for GPT-4 may not work well for Llama.

Prompting Strategies by Model

GPT-4 / Claude:

• Handle complex instructions well
• Good at following multi-step tasks
• Respond well to few-shot examples

GPT-3.5 / Claude 3 Haiku:

• Need simpler, more explicit instructions
• Benefit from output format specifications
• May need more examples than GPT-4

Open-source (Llama, Mistral):

• Need very explicit instructions
• Benefit from structured prompts (format as Q&A, input/output pairs)
• May need prompt templates specific to the model's training

Example: Same task, different prompts

For GPT-4:

You are a customer support assistant.
Answer this question based on our knowledge base:

Knowledge base: {kb_context}
Question: {question}

Provide a helpful, professional response.

For Claude 3 Haiku (more explicit):

You are a customer support assistant.

Knowledge base:
{kb_context}

Question: {question}

Instructions:
1. Find the answer in the knowledge base
2. Provide a helpful, professional response
3. If the answer isn't in the knowledge base, say "I don't have that information"
4. Keep your response under 100 words

Answer:

For Llama 3 70B (even more structured):

### Task
Answer customer questions using the knowledge base.

### Knowledge Base
{kb_context}

### Question
{question}

### Instructions
- Only use information from the knowledge base
- Be helpful and professional
- If you don't know, say "I don't have that information"
- Maximum 100 words

### Answer

Evaluate each prompt with its target model. Don't assume one prompt works for all.

Managing Multi-Model Infrastructure

Challenges

1. API Key Management You now have OpenAI, Anthropic, Google, and maybe Hugging Face keys.

Solution: Use a secrets manager (AWS Secrets Manager, HashiCorp Vault) and a unified client:

class UnifiedLLMClient:
    def __init__(self):
        self.openai_client = OpenAI(api_key=get_secret("openai"))
        self.anthropic_client = Anthropic(api_key=get_secret("anthropic"))
        self.google_client = GoogleAI(api_key=get_secret("google"))
    
    async def call(self, model: str, prompt: str, **kwargs):
        if model.startswith("gpt"):
            return await self._call_openai(model, prompt, **kwargs)
        elif model.startswith("claude"):
            return await self._call_anthropic(model, prompt, **kwargs)
        elif model.startswith("gemini"):
            return await self._call_google(model, prompt, **kwargs)
        else:
            raise ValueError(f"Unknown model: {model}")

2. Rate Limits and Quotas Each provider has different limits.

Solution: Implement fallbacks and request throttling:

from tenacity import retry, stop_after_attempt, wait_exponential

@retry(
    stop=stop_after_attempt(3),
    wait=wait_exponential(multiplier=1, min=2, max=10)
)
async def call_with_fallback(primary_model, fallback_model, prompt):
    try:
        return await llm_call(primary_model, prompt)
    except RateLimitError:
        logger.warning(f"{primary_model} rate limited, trying {fallback_model}")
        return await llm_call(fallback_model, prompt)

3. Monitoring and Observability Each model has different latency, error patterns, and costs.

Solution: Tag traces by model and task:

@evalops.trace(scenario="customer-support-qa")
async def answer_question(question: str):
    model = router.select_model("customer-support-qa", question)
    
    response = await llm_call(
        PROMPT,
        question,
        model=model,
        metadata={"model": model, "task": "customer-support-qa"}
    )
    
    return response

Now you can aggregate metrics by model:

• GPT-4: 89% accuracy, $0.012/query, 3.2s P95
• Claude Sonnet: 85% accuracy, $0.003/query, 1.5s P95

Cost Optimization Strategies

Strategy 1: Tiered Routing

Route by user tier:

• Free users → Cheap models
• Paid users → Mid-tier models
• Enterprise users → Best models

def select_model_by_tier(task: str, user_tier: str) -> str:
    model_tiers = {
        "free": {
            "customer-support": "claude-3-haiku",
            "summarization": "gpt-3.5-turbo"
        },
        "paid": {
            "customer-support": "claude-3-sonnet",
            "summarization": "gpt-4-turbo"
        },
        "enterprise": {
            "customer-support": "gpt-4-turbo",
            "summarization": "gpt-4-turbo"
        }
    }
    return model_tiers[user_tier][task]

Strategy 2: Batch Processing for Non-Urgent Tasks

Use cheaper models for batch jobs:

• Real-time chat → GPT-4 (users waiting)
• Nightly report generation → GPT-3.5 or open-source (no one waiting)

Strategy 3: Caching

Cache LLM responses for identical inputs:

import hashlib
from functools import lru_cache

@lru_cache(maxsize=10000)
async def cached_llm_call(prompt_hash: str, input_hash: str, model: str):
    # Actual LLM call
    return await llm_call(prompt, input, model)

async def call_with_cache(prompt: str, input: str, model: str):
    prompt_hash = hashlib.md5(prompt.encode()).hexdigest()
    input_hash = hashlib.md5(input.encode()).hexdigest()
    return await cached_llm_call(prompt_hash, input_hash, model)

For repeated queries (e.g., "How do I reset my password?"), this eliminates API costs entirely.

Strategy 4: Fine-Tuning for High-Volume Tasks

If you're running 100K+ queries/day on a task, consider fine-tuning a smaller model:

• Collect 1K+ examples of GPT-4 outputs on your task
• Fine-tune GPT-3.5 or an open-source model on this data
• Evaluate: does fine-tuned GPT-3.5 match GPT-4 quality?
• If yes, switch and save 95%

Example:

• Before: GPT-4 at 100K queries/day = $1,200/day
• After: Fine-tuned GPT-3.5 at 100K queries/day = $60/day
• Savings: $1,140/day = $416K/year

Evaluating Model Drift and Updates

Providers update models regularly. OpenAI has rolled out multiple GPT-4 versions. Claude versions change. Your evaluation might become outdated.

Monitor Model Versions

Log which exact model version you use:

response = openai.chat.completions.create(
    model="gpt-4-turbo-2024-04-09",  # Pin specific version
    ...
)

When providers announce updates, re-run evals:

# Compare new version to current
evalops compare \
  --baseline gpt-4-turbo-2024-04-09 \
  --candidate gpt-4-turbo-2024-08-15 \
  --dataset customer-support-qa

If quality improves, upgrade. If it regresses, stick with old version or adjust prompts.

Case Study: Multi-Model Migration

Scenario: A content platform uses GPT-4 for everything. Monthly bill: $280K.

Goal: Reduce costs by 70% without significantly impacting quality.

Approach:

Step 1: Task inventory

• Article summarization: 50K/day
• Comment moderation: 200K/day
• Content recommendations: 30K/day
• Author writing assistance: 5K/day

Step 2: Evaluation Built eval sets for each task, tested 6 models (GPT-4, GPT-3.5, Claude variants, Gemini).

Step 3: Routing decisions

Task	Volume	Current (GPT-4)	New Model	Quality Impact	Daily Savings
Summarization	50K	$3,000	Claude 3 Haiku	-3%	$2,987
Moderation	200K	$12,000	Gemini Pro + GPT-4 cascade	-1%	$11,400
Recommendations	30K	$1,800	GPT-3.5	-5%	$1,620
Writing assistance	5K	$300	GPT-4 (keep)	0%	$0

Step 4: Implementation Rolled out routing layer with fallbacks. Monitored for 2 weeks.

Results:

• Daily cost: $9,333 → $2,626 (72% reduction)
• Quality: Minimal impact (-2% average across tasks)
• User satisfaction: Unchanged (no complaints)
• Annual savings: $2.45M

Conclusion

There is no "best" LLM—only the best model for each specific task. Multi-model evaluation lets you match workloads to models systematically:

• Measure quality, cost, and latency for every candidate model
• Route tasks to the optimal model based on requirements
• Use dynamic routing for variable-complexity tasks
• Implement fallbacks for resilience
• Monitor and re-evaluate as models evolve

The result: better quality, lower costs, and more robustness than any single-model approach.

Start small:

1. Pick your highest-cost task
2. Evaluate 3 alternative models
3. Compare cost-quality tradeoffs
4. Switch the task with the clearest win
5. Repeat for other tasks

Within a quarter, you'll have a sophisticated multi-model system saving 50-80% on LLM costs while maintaining or improving quality.

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Next Steps:

• Run your first multi-model evaluation with EvalOps
• Explore model routing patterns in Spellbook
• Join the community to discuss model selection strategies

Questions about optimizing your model mix? Email hello@evalops.dev.