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Head-to-Head Model Comparison

Gemma 4 E2B vs Claude Opus 5.5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs 연구팀 검토 완료최종 검토일: October 2026편집 정책
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GoogleAnthropicopen-sourceflagship

Compare Gemma 4 E2B by Google against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.

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Google

Gemma 4 E2B

오픈 웨이트 파운데이션 모델

Google의 Gemma 4 E2B 모델은 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보에 최적화된 AI 모델로, 131K 컨텍스트 윈도우, 초당 260 토큰의 생성 속도, 종합 지능 지수 55/100점(무료 오픈 웨이트 제공)을 제공합니다.

Intelligence Index55 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

N/A

Anthropic

Claude Opus 5.5

플래그십 프론티어 모델

Anthropic의 Claude Opus 5.5 모델은 최첨단 엔터프라이즈 추론, 소프트웨어 개발 및 핵심 프로덕션 워크로드에 최적화된 AI 모델로, 1M 컨텍스트 윈도우, 초당 78 토큰의 생성 속도, 종합 지능 지수 94/100점(1M 토큰당 입력 $4 / 출력 $20)을 제공합니다.

Intelligence Index94 / 100
Input Token:

$4.00 / 1M

Output Token:

$20.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

84.6%

한눈에 보는 핵심 차이점

  • Gemma 4 E2B is 1.0x cheaper for API usage (input + output tokens combined).
  • Claude Opus 5.5 has a larger context window (1M vs 131K tokens).
  • Gemma 4 E2B is faster at 260 tok/s vs 78 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGemma 4 E2BClaude Opus 5.5
Intelligence Index
Composite reasoning & knowledge score (0-100)
5594
Provider
GoogleAnthropic
Input Price
Cost per million input tokens
$0.00 / 1M$4.00 / 1M
Output Price
Cost per million output tokens
$0.00 / 1M$20.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.00 / 1M$3.08 / 1M
Context Window
Maximum input + output tokens
131.1K tokens~197 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
8K128K
Throughput Speed
Tokens generated per second
260 tok/s78 tok/s
SWE-bench Verified
Real-world software engineering
N/A84.6%
GPQA Diamond
Graduate-level reasoning
N/A82.3%
Cached Input Price
Prompt caching discount per 1M tokens
$0.00 / 1M$0.40 / 1M

부문별 우위 모델

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Gemma 4 E2B
1.0x cheaper monthly runtime
Speed
Gemma 4 E2B
+233% faster generation
Context
Claude Opus 5.5
1M tokens window

검증된 벤치마크 점수 비교

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

Gemma 4 E2BClaude Opus 5.5
SWE-bench Verified (Autonomous Coding)

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

N/Avs84.6%
GPQA Diamond (Doctorate-Level Science & Math)

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

N/Avs82.3%
MMLU-Pro (Multi-task Language Understanding)

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

67.2%vs91.5%
LMSYS Chatbot Arena Elo

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

1260ptsvs1507pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs19.8%
Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs66.4%
SciCode (Scientific Computing)

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

N/Avs58.7%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs83.1%
AIME (Advanced Math Competition)

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

N/Avs95.4%
Long-Context Reasoning

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

N/Avs92.6%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

260 tok/s vs78 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 4 E2B0.18s
Claude Opus 5.51.40s
Est. End-to-End Response (500 tokens)TTFT + generation time
Gemma 4 E2B2.10s
Claude Opus 5.57.81s

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)
Gemma 4 E2B Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
Claude Opus 5.5 Monthly API Bill
$24.60 / month
$4/1M in · $20/1M out
Gemma 4 E2B saves approximately $24.60 / month (100%) under this workload.1.0x Savings

Gemma 4 E2B · Strengths & Key Advantages

  • 131K 컨텍스트 윈도우 및 최대 8K 출력 토큰 생성 지원 (MMLU-Pro 67.2% · Arena Elo 1260)
  • 초당 260 토큰의 빠른 처리 속도와 180ms에 불과한 첫 토큰 지연 시간(TTFT)
  • 오픈 웨이트 아키텍처 (E2B (2B))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

Claude Opus 5.5 · Strengths & Key Advantages

  • 1M 컨텍스트 윈도우 및 최대 128K 출력 토큰 생성 지원 (SWE-bench Verified 84.6% · GPQA Diamond 82.3%)
  • 초당 78 토큰의 빠른 처리 속도와 1400ms에 불과한 첫 토큰 지연 시간(TTFT)
  • 합리적인 API 요금 체계(1M 토큰당 입력 $4 / 출력 $20)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
  • 대규모 프로덕션 호출 환경에서는 비용 절감을 위해 프롬프트 캐싱과 배치 API(Batch API) 활용이 권장됩니다

Token Use & Task Economics

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

MetricGemma 4 E2BClaude Opus 5.5
Cost per Task
Estimated USD to finish one task
$0.00$3.85
Output Tokens / Task
Tokens generated per task
2K68K
Reasoning Tokens / Task
Thinking tokens burned per task
1K42K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

FeatureGemma 4 E2BClaude Opus 5.5
Official API Model ID (SDK)models/gemma-4-e2bclaude-opus-5-5-20260901
API ProtocolGoogle Gemini API (generateContent) + OpenAI-Compatible SDKAnthropic Messages API + AWS Bedrock + Vertex AI
Release Date2026-042026-09
Knowledge CutoffJanuary 2026June 2026
ArchitectureUltra-compact edge transformerExtended Autonomous Hybrid Reasoning Transformer
ParametersE2B (2B)Confidential
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Free across full context window (only KV-cache VRAM scales)Standard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)$0.4/1M cached read (90% off); 5-min default & 1-hour extended TTL
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxBuilt-in Google Search Grounding + Managed Python Code Execution SandboxNative Web Search Citations + Code Execution Tool + MCP Connectors
Official Fine-Tuning SupportFull Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)Custom Model Tuning via Amazon Bedrock & Prompt Distillation
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 2 GB VRAM (4-bit Quantized) · 4 GB VRAM (Native BF16)Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier ClassificationGoogle DeepMind Frontier Safety Framework VerifiedAnthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated
Enterprise Compliance (ZDR/HIPAA/SOC2)100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 ControlSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestext, visiontext, vision
Output Modalitiestexttext

Gemma 4 E2B vs Claude Opus 5.5: 핵심 요약 문답 (FAQ)

Which is more intelligent, Gemma 4 E2B or Claude Opus 5.5?

Claude Opus 5.5 is more intelligent. It scores 94 compared with 55 on the composite Intelligence Index.

Which model is faster?

Gemma 4 E2B is faster, generating 260 tokens per second compared with 78 tokens per second.

Which model is cheaper to run?

Gemma 4 E2B is cheaper at $0.00 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemma 4 E2B has lower latency, with a time to first token of 0.18s compared with 1.40s.

Which model has a larger context window?

Claude Opus 5.5 has a larger context window, supporting 1M tokens compared with 131.1K tokens.

종합 평가

Choose Gemma 4 E2B for 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적., or choose Claude Opus 5.5 for 최첨단 엔터프라이즈 추론, 소프트웨어 개발 및 핵심 프로덕션 워크로드, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적..

자주 묻는 질문 (FAQ)

Which is cheaper: Gemma 4 E2B or Claude Opus 5.5?+

Gemma 4 E2B costs $0/1M input and $0/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.

Which model has a larger context window: Gemma 4 E2B or Claude Opus 5.5?+

Gemma 4 E2B supports 131K tokens whereas Claude Opus 5.5 supports 1M 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.

공식 출처: Google & Anthropic Official Documentation