ai-model-reference

AI Model Reference Guide (2025년 12월 기준)

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AI Model Reference Guide (2025년 12월 기준)

AI 모델 API 호출 시 정확한 모델명과 가격 정보를 빠르게 참조할 수 있는 가이드입니다.

🚀 Quick Reference - 즉시 사용 가능한 모델명

🧠 추론 모델 (복잡한 문제 해결)

제공사 모델 API 호출명 Context Input/Output (/1M)

OpenAI o3 o3-2025-04-16

200K $2.00 / $8.00

OpenAI o4-mini o4-mini-2025-04-16

200K $1.10 / $4.40

DeepSeek R1 deepseek-reasoner

64K $0.55 / $2.19

Google Gemini 3 Pro gemini-3-pro-preview

1M $2.00~$4.00 / $12.00~$18.00

Google Gemini 2.5 Pro gemini-2.5-pro

1M $1.25 / $10.00

Anthropic Opus 4.5 claude-opus-4-5-20251101

200K $5.00 / $25.00

⚡ FAST 모델 (일반 작업)

제공사 모델 API 호출명 Context Input/Output (/1M)

OpenAI GPT-5.1 gpt-5.1

272K $1.25 / $10.00

OpenAI GPT-5 gpt-5-2025-08-07

272K $1.25 / $10.00

OpenAI GPT-5 최신 gpt-5-chat-latest

272K $1.25 / $10.00

OpenAI GPT-4.1 gpt-4.1-2025-04-14

1M $2.00 / $8.00

Anthropic Sonnet 4.5 claude-sonnet-4-5-20250929

200K $3.00 / $15.00

Anthropic Sonnet 4 claude-sonnet-4-20250514

200K $3.00 / $15.00

Google Gemini 2.5 Flash gemini-2.5-flash

1M $0.15 / $0.60~$3.50

💰 가성비 모델 (대량 처리/저비용)

제공사 모델 API 호출명 Context Input/Output (/1M)

OpenAI GPT-5 Nano gpt-5-nano

272K $0.05 / $0.40

OpenAI GPT-4o Mini gpt-4o-mini

128K $0.15 / $0.60

OpenAI GPT-4.1 Nano gpt-4.1-nano-2025-04-14

1M $0.10 / $0.40

Google Gemini 2.5 Flash-Lite gemini-2.5-flash-lite

1M $0.10 / $0.40

Google Gemini 2.0 Flash-Lite gemini-2.0-flash-lite

1M $0.075 / $0.30

Anthropic Haiku 3 claude-3-haiku-20240307

200K $0.25 / $1.25

DeepSeek Chat deepseek-chat

64K $0.27 / $1.10

📏 Context Window 비교

제공사 최대 Context 대표 모델

Google 1M (1,048,576) Gemini 2.5 시리즈 전체

OpenAI 1M GPT-4.1 시리즈

OpenAI 272K GPT-5 시리즈

Anthropic 200K Claude 전체

DeepSeek 64K R1, Chat

상세 정보 참조

  • 전체 모델 목록 및 API 호출명: references/models.md

  • 상세 가격 및 캐싱 비용: references/pricing.md

빠른 선택 가이드

복잡한 추론/코딩 작업

OpenAI: o3, o4-mini Anthropic: claude-opus-4-5-20251101, claude-opus-4-20250514 Google: gemini-3-pro-preview, gemini-2.5-pro DeepSeek: deepseek-reasoner

빠른 응답이 필요한 일반 작업

OpenAI: gpt-5.1, gpt-5, gpt-4o Anthropic: claude-sonnet-4-5-20250929, claude-sonnet-4-20250514 Google: gemini-2.5-flash

대량 처리/비용 최적화

OpenAI: gpt-5-nano ($0.05/$0.40) Anthropic: claude-3-5-haiku-20241022 ($0.80/$4.00) Google: gemini-2.5-flash-lite ($0.10/$0.40) DeepSeek: deepseek-chat (off-peak 75% 할인)

비용 절감 전략

  1. 프롬프트 캐싱 (90% 절감 가능)
  • Anthropic: cache write 1.25x, cache read 0.1x (90% 절감)

  • OpenAI: cached input $0.125/1M (GPT-5 기준 90% 절감)

  • Google: cache read 10% of base price

  1. 배치 처리 (50% 절감)
  • 24시간 내 비동기 처리로 입출력 50% 할인

  • OpenAI Batch API, Anthropic Batch Processing 지원

  1. 모델 계층화 전략

간단한 작업 → Nano/Haiku (저비용) ↓ 복잡도 증가 시 중간 작업 → Mini/Flash (균형) ↓ 복잡도 증가 시 복잡한 작업 → Pro/Opus (고성능)

코드 예시

OpenAI API 호출

from openai import OpenAI client = OpenAI()

GPT-5.1 (최신 플래그십)

response = client.chat.completions.create( model="gpt-5.1", # 또는 "gpt-5-2025-08-07" messages=[{"role": "user", "content": "Hello"}] )

추론 모델

response = client.chat.completions.create( model="o3-2025-04-16", messages=[{"role": "user", "content": "복잡한 수학 문제"}] )

Anthropic API 호출

import anthropic client = anthropic.Anthropic()

Claude Opus 4.5 (최신 플래그십)

response = client.messages.create( model="claude-opus-4-5-20251101", max_tokens=1024, messages=[{"role": "user", "content": "Hello"}] )

가성비 모델

response = client.messages.create( model="claude-3-5-haiku-20241022", max_tokens=1024, messages=[{"role": "user", "content": "간단한 질문"}] )

Google Gemini API 호출

import google.generativeai as genai

Gemini 3 Pro (최신 추론 모델)

model = genai.GenerativeModel('gemini-3-pro-preview') response = model.generate_content("Hello")

Gemini 2.5 Pro

model = genai.GenerativeModel('gemini-2.5-pro') response = model.generate_content("Hello")

Gemini 2.5 Flash (빠른 응답)

model = genai.GenerativeModel('gemini-2.5-flash') response = model.generate_content("Hello")

DeepSeek API 호출

from openai import OpenAI

client = OpenAI( api_key="your-deepseek-key", base_url="https://api.deepseek.com" )

DeepSeek Chat (일반)

response = client.chat.completions.create( model="deepseek-chat", messages=[{"role": "user", "content": "Hello"}] )

DeepSeek Reasoner (추론)

response = client.chat.completions.create( model="deepseek-reasoner", messages=[{"role": "user", "content": "복잡한 문제"}] )

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