LLM Model Selection Guide: Finding the Optimal Model by Cost vs. Performance

The idea that “you should just use the best model” comes back as a cost bomb in production. In practice, the key is choosing the right model for the task.

Cost and Performance Comparison of Major Models (as of 2025)

Model	Input ($/1M tokens)	Output ($/1M tokens)	Context	Characteristics
GPT-4o	$2.50	$10.00	128K	Multimodal, general-purpose
GPT-4o mini	$0.15	$0.60	128K	Lightweight, fast
Claude Opus 4.7	$15.00	$75.00	200K	Best reasoning
Claude Sonnet 4.6	$3.00	$15.00	200K	Balanced, strong at code
Claude Haiku 4.5	$0.80	$4.00	200K	Ultra-lightweight, low latency
Gemini 1.5 Flash	$0.075	$0.30	1M	Lowest cost, long-context processing

Recommended Models by Task Type

Simple Classification and Extraction

Python

from openai import OpenAI
client = OpenAI()

def classify_sentiment(text):
    # Sentiment classification is enough with gpt-4o-mini — 17x cheaper than gpt-4o
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        max_tokens=10,
        messages=[{
            "role": "user",
            "content": f"Classify the sentiment of the following text as only one of positive/negative/neutral:
{text}"
        }]
    )
    return response.choices[0].message.content.strip()

At 100,000 requests per day, switching from GPT-4o to GPT-4o mini alone can save millions of KRW per month.

Code Generation and Review

Python

import anthropic
client = anthropic.Anthropic()

def review_code(code):
    # Claude Sonnet excels at code tasks
    response = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=1000,
        messages=[{
            "role": "user",
            "content": f"Find bugs and improvements in the following code:

{code}

        }]
    )
    return response.content[0].text

Complex Reasoning and Analysis

For business strategy analysis, legal document interpretation, and complex math, use a top-tier model.

Python

def deep_analysis(document):
    response = client.messages.create(
        model="claude-opus-4-7",  # When maximum reasoning capability is needed
        max_tokens=4000,
        messages=[{"role": "user", "content": document}]
    )
    return response.content[0].text

Model Routing: Automatically Selecting the Right Model

Python

from openai import OpenAI
import json

client = OpenAI()

ROUTER_PROMPT = """Classify the complexity of the following user request.
- simple: simple questions, keyword extraction, translation, summarization
- medium: general writing, code generation, analysis
- complex: complex reasoning, specialized domains, creative work
Return JSON only: {"complexity": "simple|medium|complex"}"""

MODEL_MAP = {
    "simple":  "gpt-4o-mini",
    "medium":  "claude-sonnet-4-6",
    "complex": "claude-opus-4-7"
}

def route_and_run(user_query):
    routing = client.chat.completions.create(
        model="gpt-4o-mini",
        max_tokens=50,
        response_format={"type": "json_object"},
        messages=[
            {"role": "system", "content": ROUTER_PROMPT},
            {"role": "user", "content": user_query}
        ]
    )
    route = json.loads(routing.choices[0].message.content)
    return MODEL_MAP[route["complexity"]]

In production, introducing routing typically cuts overall costs by 40–60% while keeping quality nearly the same.

Cost Monitoring

Python

COST_PER_1M = {
    "gpt-4o":              {"input": 2.50,  "output": 10.00},
    "gpt-4o-mini":         {"input": 0.15,  "output": 0.60},
    "claude-opus-4-7":     {"input": 15.00, "output": 75.00},
    "claude-sonnet-4-6":   {"input": 3.00,  "output": 15.00},
    "claude-haiku-4-5-20251001": {"input": 0.80, "output": 4.00},
}

def calculate_cost(model, input_tokens, output_tokens):
    rates = COST_PER_1M.get(model, {"input": 0, "output": 0})
    return (input_tokens * rates["input"] + output_tokens * rates["output"]) / 1_000_000

In the next post, we compare the cost structures and ROI of three customization approaches: RAG, Fine-tuning, and prompt engineering.

확인 정보

#LLM #모델선택 #비용최적화 #GPT #Claude #Gemini

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LLM Model Selection Guide: Finding the Optimal Model by Cost vs. Performance