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Head-to-Head Model Comparison

Gemini Embedding 2 vs Llama 5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs 연구팀 검토 완료최종 검토일: October 2026편집 정책
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GoogleMetalightweightopen-source

Compare Gemini Embedding 2 by Google against Llama 5 by Meta across real-world coding, reasoning, token economics, and latency.

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Google

Gemini Embedding 2

벡터 임베딩 및 RAG 검색

Google의 Gemini Embedding 2 모델은 고차원 시맨틱 벡터 검색, 문서 인덱싱 및 엔터프라이즈 RAG 파이프라인에 최적화된 AI 모델로, 8K 컨텍스트 윈도우, 초당 350 토큰의 생성 속도, 종합 지능 지수 78/100점(1M 토큰당 입력 $0.2 / 출력 $0)을 제공합니다.

Intelligence Index78 / 100
Input Token:

$0.20 / 1M

Output Token:

$0.00 / 1M

Context Window:

8.2K tokens

SWE-bench Verified:

N/A

Meta

Llama 5

오픈 웨이트 파운데이션 모델

Meta의 Llama 5 모델은 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보에 최적화된 AI 모델로, 5M 컨텍스트 윈도우, 초당 65 토큰의 생성 속도, 종합 지능 지수 94/100점(무료 오픈 웨이트 제공)을 제공합니다.

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

  • Llama 5 is 1.0x cheaper for API usage (input + output tokens combined).
  • Llama 5 has a larger context window (5M vs 8K tokens).
  • Gemini Embedding 2 is faster at 350 tok/s vs 65 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGemini Embedding 2Llama 5
Intelligence Index
Composite reasoning & knowledge score (0-100)
7894
Provider
GoogleMeta
Input Price
Cost per million input tokens
$0.20 / 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
$0.04 / 1M$0.00 / 1M
Context Window
Maximum input + output tokens
8.2K tokens~12 A4 pages of text5M tokens~7,500 A4 pages of text
Max Output Tokens
Per single request
4K128K
Throughput Speed
Tokens generated per second
350 tok/s65 tok/s
SWE-bench Verified
Real-world software engineering
N/A74%
GPQA Diamond
Graduate-level reasoning
N/A84%

부문별 우위 모델

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Llama 5
1.0x cheaper monthly runtime
Speed
Gemini Embedding 2
+438% faster generation
Context
Llama 5
5M tokens window

검증된 벤치마크 점수 비교

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

Gemini Embedding 2Llama 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.

N/Avs87%
LMSYS Chatbot Arena Elo

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

1340ptsvs1485pts
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.

350 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
Gemini Embedding 20.20s
Llama 50.85s
Est. End-to-End Response (500 tokens)TTFT + generation time
Gemini Embedding 21.63s
Llama 58.54s

실시간 API 토큰 비용 시뮬레이터

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

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Gemini Embedding 2 Monthly API Bill
$0.30 / month
$0.2/1M in · $0/1M out
Llama 5 Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
Llama 5 saves approximately $0.30 / month (100%) under this workload.1.0x Savings

Gemini Embedding 2 · Strengths & Key Advantages

  • 8K 컨텍스트 윈도우 및 최대 4K 출력 토큰 생성 지원 (Arena Elo 1340)
  • 초당 350 토큰의 빠른 처리 속도와 200ms에 불과한 첫 토큰 지연 시간(TTFT)
  • 합리적인 API 요금 체계(1M 토큰당 입력 $0.2 / 출력 $0)와 프롬프트 캐싱, 함수 호출 및 정형화된 JSON 출력 완벽 지원
Considerations & Trade-offs
  • 독점 클라우드 전용 모델로 로컬 오프라인 서버에 가중치를 다운로드하여 실행할 수 없습니다
  • 초고난도 학술 연구 및 수학 증명 과제에서는 상위 대형 추론 특화 모델이 더 깊은 분석을 제공할 수 있습니다

Llama 5 · Strengths & Key Advantages

  • 5M 컨텍스트 윈도우 및 최대 128K 출력 토큰 생성 지원 (SWE-bench Verified 74% · GPQA Diamond 84%)
  • 초당 65 토큰의 빠른 처리 속도와 850ms에 불과한 첫 토큰 지연 시간(TTFT)
  • 오픈 웨이트 아키텍처 (600B+ total (~40-80B ))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.

MetricGemini Embedding 2Llama 5
Cost per Task
Estimated USD to finish one task
$0.08$0.00
Output Tokens / Task
Tokens generated per task
0K40K
Reasoning Tokens / Task
Thinking tokens burned per task
0K20K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

FeatureGemini Embedding 2Llama 5
Official API Model ID (SDK)models/gemini-embedding-2meta-llama/Llama-5
API ProtocolGoogle Gemini API (generateContent) + OpenAI-Compatible SDKOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2026-042026-04
Knowledge CutoffFebruary 2026January 2026
ArchitectureUnified multimodal joint-embedding transformerMixture-of-Experts with recursive self-improvement (~600B total, 40-80B active)
ParametersConfidential600B+ total (~40-80B active)
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPMUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Flat rate ($0.2/1M in) across full 8K windowFree across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTLImplicit + Explicit Context Caching at $0/1M with configurable TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingPrompt-based JSON extraction onlyNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxBuilt-in Google Search Grounding + Managed Python Code Execution SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportVertex AI Supervised Adapter Tuning & Context DistillationFull Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 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 VRAMCloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)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 ClassificationGoogle DeepMind Frontier Safety Framework VerifiedOpen-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, vision, audio, videotext, vision, video, audio
Output Modalitiesembeddingtext

Gemini Embedding 2 vs Llama 5: 핵심 요약 문답 (FAQ)

Which is more intelligent, Gemini Embedding 2 or Llama 5?

Llama 5 is more intelligent. It scores 94 compared with 78 on the composite Intelligence Index.

Which model is faster?

Gemini Embedding 2 is faster, generating 350 tokens per second compared with 65 tokens per second.

Which model is cheaper to run?

Llama 5 is cheaper at $0.00 per 1M tokens blended, compared with $0.04 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini Embedding 2 has lower latency, with a time to first token of 0.20s compared with 0.85s.

Which model has a larger context window?

Llama 5 has a larger context window, supporting 5M tokens compared with 8.2K tokens.

종합 평가

Choose Gemini Embedding 2 for 고차원 시맨틱 벡터 검색, 문서 인덱싱 및 엔터프라이즈 rag 파이프라인, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적., or choose Llama 5 for 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적..

자주 묻는 질문 (FAQ)

Which is cheaper: Gemini Embedding 2 or Llama 5?+

Gemini Embedding 2 costs $0.2/1M 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: Gemini Embedding 2 or Llama 5?+

Gemini Embedding 2 supports 8K 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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.

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