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.

Compare Current Models, Then Measure Your Workload

Model names, availability, limits, and prices change. As checked on 2026-09-30, use each provider's current model catalog and price page before selecting candidates. Keep the model version and price date with every evaluation result.

Compare quality, format compliance, p95 latency, and cost per 1,000 requests on the same representative test set. The model IDs below illustrate routing only; verify availability in your account before calling them.

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 — — verify the price difference on the current price pages
    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:\n{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:\n{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"]]

Cost Monitoring

Store current input and output rates outside the article and record the source and check date. Do not silently treat an unknown model as free.

Python

def calculate_cost(input_tokens, output_tokens, input_rate_per_m, output_rate_per_m):
    return (input_tokens * input_rate_per_m + output_tokens * output_rate_per_m) / 1_000_000

Comparing Candidate Models on Your Own Data

Public benchmark rankings don't reflect your input format, language, or output spec. That is the most common reason model choices go wrong.

Reproducible evaluation

Sample 100–200 real requests from logs, remove personal data, and attach answers or grading criteria. The share of Korean and long inputs should match reality.
Use the same system prompt and output-format instructions for every candidate.
Record accuracy, format compliance (e.g., JSON parses), p95 latency, and cost per 1,000 requests together.

Python

import json, time
def run(model, cases):
    ok = fmt = 0; lat = []; tok_in = tok_out = 0
    for c in cases:
        t = time.time(); r = call_llm(model, c["input"])   # common wrapper
        lat.append(time.time() - t)
        tok_in += r.usage.input_tokens; tok_out += r.usage.output_tokens
        try: out = json.loads(r.text); fmt += 1
        except ValueError: continue
        ok += grade(out, c["expected"])
    lat.sort()
    return {"acc": ok/len(cases), "format": fmt/len(cases),
            "p95": lat[int(len(lat)*0.95)-1], "tokens": (tok_in, tok_out)}

Compute cost by multiplying recorded tokens by each provider's current published price (prices change often, so keep them in config, not code).

How to read it

If accuracy differences are small, format compliance, latency, and cost decide.
If one request type shows a large gap, route only that type to the stronger model.

Prevention

Pin model versions by snapshot name, and re-run the same evaluation set before switching to a new version.

Code verification scope (2026-09-30)

Python code block syntax was checked with ast.parse on Python 3.14.6. External SDK calls, service connections, and performance claims were outside this check. Verify dependencies and model availability in the deployment environment.

Official references: OpenAI model catalog, OpenAI pricing.

#LLM #Cost optimization #GPT #Claude #Gemini

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Sources and verification

Sources and checks vary by article. Only articles with a recorded check date, scope and sources below were checked, and only within that scope. For other articles, verify against the current docs for your version before applying anything, and email us about errors. Editorial policy · email us

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Published · May 20, 2026

Topic-related official docs (auto-linked · not per-claim sources)OpenAI documentation ↗Anthropic documentation ↗

How we write and review →Editorial policy →

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