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Head-to-Head Model Comparison

Gemini 3.5 Flash-Lite vs Grok 4.6

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs 연구팀 검토 완료최종 검토일: October 2026편집 정책
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Related topics (12)
AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Gemini 3.5 Flash-Lite vs Grok 4.6: Benchmarks, Pricing & Speed Comparison

Google의 Gemini 3.5 Flash-Lite와 xAI의 Grok 4.6는 현재 가장 주목받는 AI 모델입니다. Gemini 3.5 Flash-Lite는 1.05M 토큰 컨텍스트 윈도우와 100만 토큰당 $0.40/$2.00(입력/출력)의 요금을 제공하며, Grok 4.6는 500K 토큰 컨텍스트와 $2.00/$6.00의 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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Google

Gemini 3.5 Flash-Lite

초고속 저지연 추론 모델

Google의 Gemini 3.5 Flash-Lite 모델은 초저지연 실시간 대화, 고속 라우팅 및 대규모 트래픽 처리에 최적화된 AI 모델로, 1.0M 컨텍스트 윈도우, 초당 240 토큰의 생성 속도, 종합 지능 지수 69/100점(1M 토큰당 입력 $0.4 / 출력 $2)을 제공합니다.

Intelligence Index69 / 100
Input Token:

$0.40 / 1M

Output Token:

$2.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

N/A

xAI

Grok 4.6

플래그십 프론티어 모델

xAI의 Grok 4.6 모델은 최첨단 엔터프라이즈 추론, 소프트웨어 개발 및 핵심 프로덕션 워크로드에 최적화된 AI 모델로, 500K 컨텍스트 윈도우, 초당 115 토큰의 생성 속도, 종합 지능 지수 77/100점(1M 토큰당 입력 $2 / 출력 $6)을 제공합니다.

Intelligence Index77 / 100
Input Token:

$2.00 / 1M

Output Token:

$6.00 / 1M

Context Window:

500K tokens

SWE-bench Verified:

75.8%

한눈에 보는 핵심 차이점

  • Gemini 3.5 Flash-Lite is 3.6x cheaper for API usage (input + output tokens combined).
  • Gemini 3.5 Flash-Lite has a larger context window (1M vs 500K tokens).
  • Gemini 3.5 Flash-Lite is faster at 240 tok/s vs 115 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGemini 3.5 Flash-LiteGrok 4.6
Intelligence Index
Composite reasoning & knowledge score (0-100)
6977
Provider
GooglexAI
Input Price
Cost per million input tokens
$0.40 / 1M$2.00 / 1M
Output Price
Cost per million output tokens
$2.00 / 1M$6.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.35 / 1M$1.35 / 1M
Context Window
Maximum input + output tokens
1.05M tokens~1,573 A4 pages of text500K tokens~750 A4 pages of text
Max Output Tokens
Per single request
33K66K
Throughput Speed
Tokens generated per second
240 tok/s115 tok/s
SWE-bench Verified
Real-world software engineering
N/A75.8%
GPQA Diamond
Graduate-level reasoning
N/A77.2%
Cached Input Price
Prompt caching discount per 1M tokens
$0.10 / 1M$0.50 / 1M

부문별 우위 모델

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Gemini 3.5 Flash-Lite
3.6x cheaper monthly runtime
Speed
Gemini 3.5 Flash-Lite
+109% faster generation
Context
Gemini 3.5 Flash-Lite
1.05M tokens window

검증된 벤치마크 점수 비교

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

Gemini 3.5 Flash-LiteGrok 4.6
SWE-bench Verified (Autonomous Coding)

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

N/Avs75.8%
GPQA Diamond (Doctorate-Level Science & Math)

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

N/Avs77.2%
MMLU-Pro (Multi-task Language Understanding)

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

81.2%vs88%
LMSYS Chatbot Arena Elo

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

1375ptsvs1450pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs14.8%
Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs51%
SciCode (Scientific Computing)

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

N/Avs49.2%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs75%
AIME (Advanced Math Competition)

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

N/Avs89.5%
Long-Context Reasoning

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

N/Avs89%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

240 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
Gemini 3.5 Flash-Lite0.29s
Grok 4.60.58s
Est. End-to-End Response (500 tokens)TTFT + generation time
Gemini 3.5 Flash-Lite2.37s
Grok 4.64.93s

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 3.5 Flash-Lite Monthly API Bill
$2.55 / month
$0.4/1M in · $2/1M out
Grok 4.6 Monthly API Bill
$9.15 / month
$2/1M in · $6/1M out
Gemini 3.5 Flash-Lite saves approximately $6.60 / month (72%) under this workload.3.6x Savings

Gemini 3.5 Flash-Lite · Strengths & Key Advantages

  • 1.0M 컨텍스트 윈도우 및 최대 33K 출력 토큰 생성 지원 (MMLU-Pro 81.2% · Arena Elo 1375)
  • 초당 240 토큰의 빠른 처리 속도와 290ms에 불과한 첫 토큰 지연 시간(TTFT)
  • 합리적인 API 요금 체계(1M 토큰당 입력 $0.4 / 출력 $2)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

Grok 4.6 · Strengths & Key Advantages

  • 500K 컨텍스트 윈도우 및 최대 66K 출력 토큰 생성 지원 (SWE-bench Verified 75.8% · GPQA Diamond 77.2%)
  • 초당 115 토큰의 빠른 처리 속도와 580ms에 불과한 첫 토큰 지연 시간(TTFT)
  • 합리적인 API 요금 체계(1M 토큰당 입력 $2 / 출력 $6)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.

MetricGemini 3.5 Flash-LiteGrok 4.6
Cost per Task
Estimated USD to finish one task
$0.55$0.65
Output Tokens / Task
Tokens generated per task
18K45K
Reasoning Tokens / Task
Thinking tokens burned per task
7K22K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

FeatureGemini 3.5 Flash-LiteGrok 4.6
Official API Model ID (SDK)models/gemini-3-5-flash-litegrok-4-6-latest
API ProtocolGoogle Gemini API (generateContent) + OpenAI-Compatible SDKxAI REST API (OpenAI-Compatible)
Release Date2026-072026-08
Knowledge CutoffMarch 2026April 2026
ArchitectureLightweight efficient multimodal transformer1.5T Colossus Sparse MoE
ParametersConfidential1.5T (MoE)
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)Standard rate up to 200K tokens; 2x input/output tier above 200K tokensFlat rate ($2/1M in) across full 500K window
Prompt Cache Policy & TTLImplicit + Explicit Context Caching at $0.1/1M with configurable TTLCached input at $0.5/1M tokens
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
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 SandboxReal-time Web & X (Twitter) Search Grounding + Code Execution
Official Fine-Tuning SupportVertex AI Supervised Adapter Tuning & Context DistillationAPI Prompt Engineering & RAG 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 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 ClassificationGoogle DeepMind Frontier Safety Framework VerifiedxAI Production Safety & Alignment 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
Output Modalitiestexttext

Gemini 3.5 Flash-Lite vs Grok 4.6: 핵심 요약 문답 (FAQ)

Which is more intelligent, Gemini 3.5 Flash-Lite or Grok 4.6?

Grok 4.6 is more intelligent. It scores 77 compared with 69 on the composite Intelligence Index.

Which model is faster?

Gemini 3.5 Flash-Lite is faster, generating 240 tokens per second compared with 115 tokens per second.

Which model is cheaper to run?

Gemini 3.5 Flash-Lite is cheaper at $0.35 per 1M tokens blended, compared with $1.35 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini 3.5 Flash-Lite has lower latency, with a time to first token of 0.29s compared with 0.58s.

Which model has a larger context window?

Gemini 3.5 Flash-Lite has a larger context window, supporting 1.05M tokens compared with 500K tokens.

종합 평가

Gemini 3.5 Flash-Lite(초저지연 실시간 대화, 고속 라우팅 및 대규모 트래픽 처리, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.)와 Grok 4.6(최첨단 엔터프라이즈 추론, 소프트웨어 개발 및 핵심 프로덕션 워크로드, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.) 모두 뛰어난 성능을 자랑합니다. 고난도 추론과 코딩 벤치마크에서는 Grok 4.6가 앞서며, 토큰 생성 속도에서는 Gemini 3.5 Flash-Lite가 빠르고, 대규모 API 운영 비용 면에서는 Gemini 3.5 Flash-Lite가 더 경제적입니다.

자주 묻는 질문 (FAQ)

코딩과 복잡한 추론에는 Gemini 3.5 Flash-Lite와 Grok 4.6 중 어느 모델이 더 나은가요?+

종합 지능 지수 및 코딩 벤치마크에서는 Grok 4.6가 우위를 보입니다. 상단의 SWE-bench 및 GPQA 점수를 함께 확인해 보세요.

Gemini 3.5 Flash-Lite와 Grok 4.6의 API 요금 차이는 얼마인가요?+

Gemini 3.5 Flash-Lite는 100만 토큰당 입력 $0.40 / 출력 $2.00이며, Grok 4.6는 입력 $2.00 / 출력 $6.00입니다. 혼합 비용 기준으로는 Gemini 3.5 Flash-Lite가 더 저렴합니다.

어느 모델이 더 큰 컨텍스트 윈도우와 빠른 속도를 제공하나요?+

컨텍스트 길이는 Gemini 3.5 Flash-Lite가 1.05M 토큰, Grok 4.6가 500K 토큰를 지원하며, 출력 속도 면에서는 Gemini 3.5 Flash-Lite가 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: Google 및 xAI 공식 API 문서, 시스템 카드 및 독립 벤치마크 평가 (2026).