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Head-to-Head Model Comparison

Grok 4.20 (Reasoning) vs Gemini 4 Argon

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs 연구팀 검토 완료최종 검토일: October 2026편집 정책
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Grok 4.20 (Reasoning) vs Gemini 4 Argon: Benchmarks, Pricing & Speed Comparison

xAI의 Grok 4.20 (Reasoning)와 Google의 Gemini 4 Argon는 현재 가장 주목받는 AI 모델입니다. Grok 4.20 (Reasoning)는 1M 토큰 컨텍스트 윈도우와 100만 토큰당 $3.00/$15.00(입력/출력)의 요금을 제공하며, Gemini 4 Argon는 1M 토큰 컨텍스트와 $2.00/$10.00의 요금을 제공합니다. 아래에서 벤치마크, 지연 시간 및 비용을 상세히 비교해 보세요.

1:1 모델 맞대결 비교 (VS)

원하는 AI 모델 선택 및 실시간 비교

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xAI

Grok 4.20 (Reasoning)

심층 추론 및 논리 엔진

xAI의 Grok 4.20 (Reasoning) 모델은 심층 논리 추론, 고급 수학 문제 해결 및 다단계 과학적 분석에 최적화된 AI 모델로, 1M 컨텍스트 윈도우, 초당 95 토큰의 생성 속도, 종합 지능 지수 68/100점(1M 토큰당 입력 $3 / 출력 $15)을 제공합니다.

Intelligence Index68 / 100
Input Token:

$3.00 / 1M

Output Token:

$15.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

68.2%

Google

Gemini 4 Argon

네이티브 멀티모달 AI

Google의 Gemini 4 Argon 모델은 텍스트와 이미지의 네이티브 멀티모달 이해 및 복합 문서·차트 분석에 최적화된 AI 모델로, 1M 컨텍스트 윈도우, 초당 115 토큰의 생성 속도, 종합 지능 지수 97/100점(1M 토큰당 입력 $2 / 출력 $10)을 제공합니다.

Intelligence Index97 / 100
Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

81.9%

한눈에 보는 핵심 차이점

  • Gemini 4 Argon is 1.6x cheaper for API usage (input + output tokens combined).
  • Gemini 4 Argon is faster at 115 tok/s vs 95 tok/s.
  • Gemini 4 Argon leads on SWE-bench coding (81.9% vs 68.2%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGrok 4.20 (Reasoning)Gemini 4 Argon
Intelligence Index
Composite reasoning & knowledge score (0-100)
6897
Provider
xAIGoogle
Input Price
Cost per million input tokens
$3.00 / 1M$2.00 / 1M
Output Price
Cost per million output tokens
$15.00 / 1M$10.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$2.63 / 1M$1.47 / 1M
Context Window
Maximum input + output tokens
1M tokens~1,500 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
66K1000K
Throughput Speed
Tokens generated per second
95 tok/s115 tok/s
SWE-bench Verified
Real-world software engineering
68.2%81.9%
GPQA Diamond
Graduate-level reasoning
72.8%81.5%
Cached Input Price
Prompt caching discount per 1M tokens
$0.75 / 1M$0.10 / 1M

부문별 우위 모델

At-a-glance decision framework
Coding
Gemini 4 Argon
+13.7% SWE-bench lead
Cost
Gemini 4 Argon
1.6x cheaper monthly runtime
Speed
Gemini 4 Argon
+21% faster generation
Context
Tied
1M tokens window

검증된 벤치마크 점수 비교

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

Grok 4.20 (Reasoning)Gemini 4 Argon
SWE-bench Verified (Autonomous Coding)

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

68.2%vs81.9%
GPQA Diamond (Doctorate-Level Science & Math)

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

72.8%vs81.5%
MMLU-Pro (Multi-task Language Understanding)

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

84.1%vs90.8%
LMSYS Chatbot Arena Elo

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

1395ptsvs1525pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

11.8%vs18.9%
Terminal-Bench (Agentic Coding & Terminal Use)

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

44.5%vs57.4%
SciCode (Scientific Computing)

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

41.2%vs56.4%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

66.5%vs80.5%
AIME (Advanced Math Competition)

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

86.4%vs94.8%
Long-Context Reasoning

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

89.2%vs96.4%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

95 tok/s vs115 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
Grok 4.20 (Reasoning)0.98s
Gemini 4 Argon0.85s
Est. End-to-End Response (500 tokens)TTFT + generation time
Grok 4.20 (Reasoning)6.24s
Gemini 4 Argon5.20s

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)
Grok 4.20 (Reasoning) Monthly API Bill
$19.13 / month
$3/1M in · $15/1M out
Gemini 4 Argon Monthly API Bill
$12.15 / month
$2/1M in · $10/1M out
Gemini 4 Argon saves approximately $6.97 / month (36%) under this workload.1.6x Savings

Grok 4.20 (Reasoning) · Strengths & Key Advantages

  • 1M 컨텍스트 윈도우 및 최대 66K 출력 토큰 생성 지원 (SWE-bench Verified 68.2% · GPQA Diamond 72.8%)
  • 초당 95 토큰의 빠른 처리 속도와 980ms에 불과한 첫 토큰 지연 시간(TTFT)
  • 합리적인 API 요금 체계(1M 토큰당 입력 $3 / 출력 $15)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

Gemini 4 Argon · Strengths & Key Advantages

  • 1M 컨텍스트 윈도우 및 최대 1M 출력 토큰 생성 지원 (SWE-bench Verified 81.9% · GPQA Diamond 81.5%)
  • 초당 115 토큰의 빠른 처리 속도와 850ms에 불과한 첫 토큰 지연 시간(TTFT)
  • 합리적인 API 요금 체계(1M 토큰당 입력 $2 / 출력 $10)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.

MetricGrok 4.20 (Reasoning)Gemini 4 Argon
Cost per Task
Estimated USD to finish one task
$1.45$2.45
Output Tokens / Task
Tokens generated per task
45K75K
Reasoning Tokens / Task
Thinking tokens burned per task
28K36K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

FeatureGrok 4.20 (Reasoning)Gemini 4 Argon
Official API Model ID (SDK)grok-4-20-reasoning-latestmodels/gemini-4-argon
API ProtocolxAI REST API (OpenAI-Compatible)Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date2026-032026-09
Knowledge CutoffNovember 2025June 2026
ArchitectureColossus-Scale Reasoning Sparse MoEPathways Multimodal Sparse Mixture-of-Experts
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($3/1M in) across full 1000K windowStandard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLCached input at $0.75/1M tokensImplicit + Explicit Context Caching at $0.1/1M with configurable TTL
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)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 & SandboxReal-time Web & X (Twitter) Search Grounding + Code ExecutionBuilt-in Google Search Grounding + Managed Python Code Execution Sandbox
Official Fine-Tuning SupportAPI Prompt Engineering & RAG DistillationVertex AI Supervised Adapter Tuning & Context 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) · 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)Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier ClassificationxAI Production Safety & Alignment VerifiedGoogle DeepMind Frontier Safety Framework Verified
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise TierSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestext, visiontext, vision, audio, video
Output Modalitiestexttext

Grok 4.20 (Reasoning) vs Gemini 4 Argon: 핵심 요약 문답 (FAQ)

Which is more intelligent, Grok 4.20 (Reasoning) or Gemini 4 Argon?

Gemini 4 Argon is more intelligent. It scores 97 compared with 68 on the composite Intelligence Index.

Which model is faster?

Gemini 4 Argon is faster, generating 115 tokens per second compared with 95 tokens per second.

Which model is cheaper to run?

Gemini 4 Argon is cheaper at $1.47 per 1M tokens blended, compared with $2.63 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini 4 Argon has lower latency, with a time to first token of 0.85s compared with 0.98s.

종합 평가

Grok 4.20 (Reasoning)(심층 논리 추론, 고급 수학 문제 해결 및 다단계 과학적 분석, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.)와 Gemini 4 Argon(텍스트와 이미지의 네이티브 멀티모달 이해 및 복합 문서·차트 분석, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.) 모두 뛰어난 성능을 자랑합니다. 고난도 추론과 코딩 벤치마크에서는 Gemini 4 Argon가 앞서며, 토큰 생성 속도에서는 Gemini 4 Argon가 빠르고, 대규모 API 운영 비용 면에서는 Gemini 4 Argon가 더 경제적입니다.

자주 묻는 질문 (FAQ)

코딩과 복잡한 추론에는 Grok 4.20 (Reasoning)와 Gemini 4 Argon 중 어느 모델이 더 나은가요?+

종합 지능 지수 및 코딩 벤치마크에서는 Gemini 4 Argon가 우위를 보입니다. 상단의 SWE-bench 및 GPQA 점수를 함께 확인해 보세요.

Grok 4.20 (Reasoning)와 Gemini 4 Argon의 API 요금 차이는 얼마인가요?+

Grok 4.20 (Reasoning)는 100만 토큰당 입력 $3.00 / 출력 $15.00이며, Gemini 4 Argon는 입력 $2.00 / 출력 $10.00입니다. 혼합 비용 기준으로는 Gemini 4 Argon가 더 저렴합니다.

어느 모델이 더 큰 컨텍스트 윈도우와 빠른 속도를 제공하나요?+

컨텍스트 길이는 Grok 4.20 (Reasoning)가 1M 토큰, Gemini 4 Argon가 1M 토큰를 지원하며, 출력 속도 면에서는 Gemini 4 Argon가 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 & 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: xAI 및 Google 공식 API 문서, 시스템 카드 및 독립 벤치마크 평가 (2026).