Head-to-Head Model Comparison

Llama 3.2 3B vs Qwen 2.5 72B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs 연구팀 검토 완료최종 검토일: October 2026편집 정책

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Llama 3.2 3BProfile

Qwen 2.5 72BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Llama 3.2 3B

오픈 웨이트 파운데이션 모델

Meta의 Llama 3.2 3B 모델은 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보에 최적화된 AI 모델로, 128K 컨텍스트 윈도우, 초당 240 토큰의 생성 속도, 종합 지능 지수 52/100점(무료 오픈 웨이트 제공)을 제공합니다.

Intelligence Index52 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

N/A

Alibaba

Qwen 2.5 72B

오픈 웨이트 파운데이션 모델

Alibaba의 Qwen 2.5 72B 모델은 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보에 최적화된 AI 모델로, 128K 컨텍스트 윈도우, 초당 64 토큰의 생성 속도, 종합 지능 지수 44/100점(1M 토큰당 입력 $0.35 / 출력 $0.7)을 제공합니다.

Intelligence Index44 / 100

Input Token:

$0.35 / 1M

Output Token:

$0.70 / 1M

Context Window:

128K tokens

SWE-bench Verified:

45.2%

한눈에 보는 핵심 차이점

Llama 3.2 3B is 1.0x cheaper for API usage (input + output tokens combined).
Llama 3.2 3B is faster at 240 tok/s vs 64 tok/s.

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	Llama 3.2 3B	Qwen 2.5 72B
Intelligence Index Composite reasoning & knowledge score (0-100)	52	44
Provider	Meta	Alibaba
Input Price Cost per million input tokens	$0.00 / 1M	$0.35 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$0.70 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$0.19 / 1M
Context Window Maximum input + output tokens	128K tokens~192 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	4K	8K
Throughput Speed Tokens generated per second	240 tok/s	64 tok/s
SWE-bench Verified Real-world software engineering	N/A	45.2%
GPQA Diamond Graduate-level reasoning	N/A	52.8%
Cached Input Price Prompt caching discount per 1M tokens	$0.00 / 1M	$0.07 / 1M

부문별 우위 모델

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Llama 3.2 3B

1.0x cheaper monthly runtime

Speed

Llama 3.2 3B

+275% faster generation

Context

Tied

128K tokens window

검증된 벤치마크 점수 비교

Official evaluation metrics verified by independent third-party research labs.

Llama 3.2 3BQwen 2.5 72B

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

N/Avs45.2%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

N/Avs52.8%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

56.5%vs71.4%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1190ptsvs1282pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

N/Avs7.2%

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

N/Avs28.1%

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

N/Avs28.9%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs46.5%

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

N/Avs59.2%

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

N/Avs66.8%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

240 tok/s vs64 tok/s

지연 시간(Latency) 및 응답 속도

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

Llama 3.2 3B0.16s

Qwen 2.5 72B0.61s

Est. End-to-End Response (500 tokens)TTFT + generation time

Llama 3.2 3B2.24s

Qwen 2.5 72B8.42s

실시간 API 토큰 비용 시뮬레이터

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Llama 3.2 3B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Qwen 2.5 72B Monthly API Bill

$1.26 / month

$0.35/1M in · $0.7/1M out

Llama 3.2 3B saves approximately $1.26 / month (100%) under this workload.1.0x Savings

Llama 3.2 3B · Strengths & Key Advantages

128K 컨텍스트 윈도우 및 최대 4K 출력 토큰 생성 지원 (MMLU-Pro 56.5% · Arena Elo 1190)
초당 240 토큰의 빠른 처리 속도와 160ms에 불과한 첫 토큰 지연 시간(TTFT)
오픈 웨이트 아키텍처 (3B)를 통해 vLLM, SGLang, Ollama 기반 자체 GPU 서버 구축 및 파인튜닝 가능

Considerations & Trade-offs

비양자화 전체 정밀도로 로컬 서버에서 구동하려면 충분한 GPU VRAM 용량이 필요합니다
초고난도 학술 연구 및 수학 증명 과제에서는 상위 대형 추론 특화 모델이 더 깊은 분석을 제공할 수 있습니다

Qwen 2.5 72B · Strengths & Key Advantages

128K 컨텍스트 윈도우 및 최대 8K 출력 토큰 생성 지원 (SWE-bench Verified 45.2% · GPQA Diamond 52.8%)
초당 64 토큰의 빠른 처리 속도와 610ms에 불과한 첫 토큰 지연 시간(TTFT)
오픈 웨이트 아키텍처 (72B)를 통해 vLLM, SGLang, Ollama 기반 자체 GPU 서버 구축 및 파인튜닝 가능

Considerations & Trade-offs

비양자화 전체 정밀도로 로컬 서버에서 구동하려면 충분한 GPU VRAM 용량이 필요합니다
초고난도 학술 연구 및 수학 증명 과제에서는 상위 대형 추론 특화 모델이 더 깊은 분석을 제공할 수 있습니다

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	Llama 3.2 3B	Qwen 2.5 72B
Cost per Task Estimated USD to finish one task	$0.00	$0.28
Output Tokens / Task Tokens generated per task	3K	32K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

Feature	Llama 3.2 3B	Qwen 2.5 72B
Official API Model ID (SDK)	meta-llama/Llama-3.2-3B	qwen-2-5-72b-instruct
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2024-09	2024-09
Knowledge Cutoff	December 2023	September 2024
Architecture	Pruned and distilled compact transformer	Dense Autoregressive Transformer
Parameters	3B	72B
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)	Free across full context window (only KV-cache VRAM scales)	Flat rate ($0.35/1M in) across full 128K window
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Cached input at $0.07/1M tokens
Computer Use / GUI Agent	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
MCP & Parallel Tool Calling	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & Sandbox	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning Support	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token Billing	Text BPE (~4 chars/token)	Text BPE (~4 chars/token)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 2 GB VRAM (4-bit Quantized) · 6 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 45 GB VRAM (4-bit Quantized) · 155 GB VRAM (Native BF16)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalities	text	text
Output Modalities	text	text

Llama 3.2 3B vs Qwen 2.5 72B: 핵심 요약 문답 (FAQ)

Which is more intelligent, Llama 3.2 3B or Qwen 2.5 72B?

Llama 3.2 3B is more intelligent. It scores 52 compared with 44 on the composite Intelligence Index.

Which model is faster?

Llama 3.2 3B is faster, generating 240 tokens per second compared with 64 tokens per second.

Which model is cheaper to run?

Llama 3.2 3B is cheaper at $0.00 per 1M tokens blended, compared with $0.19 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Llama 3.2 3B has lower latency, with a time to first token of 0.16s compared with 0.61s.

종합 평가

Llama 3.2 3B(프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.)와 Qwen 2.5 72B(프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.) 모두 뛰어난 성능을 자랑합니다. 고난도 추론과 코딩 벤치마크에서는 Llama 3.2 3B가 앞서며, 토큰 생성 속도에서는 Llama 3.2 3B가 빠르고, 대규모 API 운영 비용 면에서는 Llama 3.2 3B가 더 경제적입니다.

자주 묻는 질문 (FAQ)

코딩과 복잡한 추론에는 Llama 3.2 3B와 Qwen 2.5 72B 중 어느 모델이 더 나은가요?+

종합 지능 지수 및 코딩 벤치마크에서는 Llama 3.2 3B가 우위를 보입니다. 상단의 SWE-bench 및 GPQA 점수를 함께 확인해 보세요.

Llama 3.2 3B와 Qwen 2.5 72B의 API 요금 차이는 얼마인가요?+

Llama 3.2 3B는 100만 토큰당 입력 $0.00 / 출력 $0.00이며, Qwen 2.5 72B는 입력 $0.35 / 출력 $0.70입니다. 혼합 비용 기준으로는 Llama 3.2 3B가 더 저렴합니다.

어느 모델이 더 큰 컨텍스트 윈도우와 빠른 속도를 제공하나요?+

컨텍스트 길이는 Llama 3.2 3B가 128K 토큰, Qwen 2.5 72B가 128K 토큰를 지원하며, 출력 속도 면에서는 Llama 3.2 3B가 더 빠릅니다.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Llama 3.2 3B vs Qwen 2.5 72B

원하는 AI 모델 선택 및 실시간 비교

Llama 3.2 3B

Qwen 2.5 72B

부문별 우위 모델

검증된 벤치마크 점수 비교

지연 시간(Latency) 및 응답 속도

실시간 API 토큰 비용 시뮬레이터

Llama 3.2 3B · Strengths & Key Advantages

Considerations & Trade-offs

Qwen 2.5 72B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

기술 사양 (Specifications)

Llama 3.2 3B vs Qwen 2.5 72B: 핵심 요약 문답 (FAQ)

종합 평가

자주 묻는 질문 (FAQ)

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