Head-to-Head Model Comparison

Gemma 3n vs Llama 5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs 연구팀 검토 완료최종 검토일: October 2026편집 정책

원하는 AI 모델 선택 및 실시간 비교

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Gemma 3nProfile

Llama 5Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Live comparison: no in-depth editorial review has been published for this pair yet.

Popular Matchups:

Google

Gemma 3n

오픈 웨이트 모델

Google의 Gemma 3n 모델은 32K 토큰 컨텍스트 윈도우와 입력 $0/1M 토큰, 출력 $0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.

Intelligence Index52 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

32K tokens

SWE-bench Verified:

N/A

Llama 5

오픈 웨이트 모델

Meta의 Llama 5 모델은 5M 토큰 컨텍스트 윈도우와 입력 $0/1M 토큰, 출력 $0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.

Intelligence Index94 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

5M tokens

SWE-bench Verified:

74%

한눈에 보는 핵심 차이점

Both models cost the same under this workload — choose on capability, not price.
Llama 5 has a larger context window (5M vs 32K tokens).
Gemma 3n is faster at 150 tok/s vs 65 tok/s.

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

Feature	Gemma 3n	Llama 5
Intelligence Index Composite reasoning & knowledge score (0-100)	52	94
Provider	Google	Meta
Input Price Cost per million input tokens	$0.00 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$0.00 / 1M
Context Window Maximum input + output tokens	32K tokens~48 A4 pages of text	5M tokens~7,500 A4 pages of text
Max Output Tokens Per single request	4K	128K
Throughput Speed Tokens generated per second	150 tok/s	65 tok/s
SWE-bench Verified Real-world software engineering	N/A	74%
GPQA Diamond Graduate-level reasoning	N/A	84%

부문별 우위 모델

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Tied

Identical monthly runtime cost

Speed

Gemma 3n

+131% faster generation

Context

Llama 5

5M tokens window

검증된 벤치마크 점수 비교

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

Gemma 3nLlama 5

SWE-bench Verified (Autonomous Coding)

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

N/Avs74%

GPQA Diamond (Doctorate-Level Science & Math)

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

N/Avs84%

MMLU-Pro (Multi-task Language Understanding)

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

64%vs87%

LMSYS Chatbot Arena Elo

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

1220ptsvs1485pts

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs68%

AIME (Advanced Math Competition)

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

N/Avs88%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

150 tok/s vs65 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

Gemma 3n0.22s

Llama 50.85s

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

Gemma 3n3.55s

Llama 58.54s

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)

Gemma 3n Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Llama 5 Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

These models cost the same under this workload — no savings either way.Break-even

Gemma 3n · Strengths & Key Advantages

Google의 첨단 Edge-optimized multimodal transformer with audio support 아키텍처와 최대 32K 토큰 컨텍스트 지원
경쟁력 있는 API 가격(입력 $0/1M, 출력 $0/1M)과 빠른 응답 속도
코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘

Considerations & Trade-offs

대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨

Llama 5 · Strengths & Key Advantages

Meta의 첨단 Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) 아키텍처와 최대 5M 토큰 컨텍스트 지원
경쟁력 있는 API 가격(입력 $0/1M, 출력 $0/1M)과 빠른 응답 속도
코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘

Considerations & Trade-offs

대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨

Token Use & Task Economics

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

Metric	Gemma 3n	Llama 5
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	3K	40K
Reasoning Tokens / Task Thinking tokens burned per task	0K	20K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

Feature	Gemma 3n	Llama 5
Official API Model ID (SDK)	models/gemma-3n	meta-llama/Llama-5
API Protocol	Google Gemini API (generateContent) + OpenAI-Compatible SDK	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-06	2026-04
Knowledge Cutoff	March 2025	January 2026
Architecture	Edge-optimized multimodal transformer with audio support	Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active)
Parameters	2B / 5B	600B+ total (~40-80B active)
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)	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI Agent	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	Supported (Screen coordinate grounding, browser & desktop GUI action loops)
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	Built-in Google Search Grounding + Managed Python Code Execution Sandbox	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) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec)	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 2 GB VRAM (4-bit Quantized) · 4 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 372 GB VRAM (4-bit Quantized) · 1290 GB VRAM (Native BF16)
Safety & Frontier Classification	Google DeepMind Frontier Safety Framework Verified	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, vision, audio	text, vision, video, audio
Output Modalities	text	text

Gemma 3n vs Llama 5: 핵심 요약 문답 (FAQ)

Which is more intelligent, Gemma 3n or Llama 5?

Llama 5 is more intelligent. It scores 94 compared with 52 on the composite Intelligence Index.

Which model is faster?

Gemma 3n is faster, generating 150 tokens per second compared with 65 tokens per second.

Which model has lower latency?

Gemma 3n has lower latency, with a time to first token of 0.22s compared with 0.85s.

Which model has a larger context window?

Llama 5 has a larger context window, supporting 5M tokens compared with 32K tokens.

종합 평가

Choose Gemma 3n for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 google 기반 프로덕션 파이프라인, or choose Llama 5 for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 meta 기반 프로덕션 파이프라인.

자주 묻는 질문 (FAQ)

Which is cheaper: Gemma 3n or Llama 5?+

Gemma 3n costs $0/1M input and $0/1M output tokens, while Llama 5 costs $0/1M input and $0/1M output tokens.

Which model has a larger context window: Gemma 3n or Llama 5?+

Gemma 3n supports 32K tokens whereas Llama 5 supports 5M tokens.

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)

Gemma 3n vs Llama 5

원하는 AI 모델 선택 및 실시간 비교

Gemma 3n

Llama 5

부문별 우위 모델

검증된 벤치마크 점수 비교

지연 시간(Latency) 및 응답 속도

실시간 API 토큰 비용 시뮬레이터

Gemma 3n · Strengths & Key Advantages

Considerations & Trade-offs

Llama 5 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

기술 사양 (Specifications)

Gemma 3n vs Llama 5: 핵심 요약 문답 (FAQ)

종합 평가

자주 묻는 질문 (FAQ)

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