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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

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial
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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

Gemini 3.5 Flash-Lite dari Google dan Grok 4.6 dari xAI adalah dua model AI unggulan. Gemini 3.5 Flash-Lite menawarkan jendela konteks 1.05M token dengan harga $0.40/$2.00 per 1 juta token, sedangkan Grok 4.6 menyediakan konteks 500K token seharga $2.00/$6.00 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.

Perbandingan Langsung (VS)

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Google

Gemini 3.5 Flash-Lite

Inferensi Super Cepat

Gemini 3.5 Flash-Lite dari Google adalah model AI yang dirancang untuk interaksi real-time dengan latensi sangat rendah dan routing bervolume tinggi, menghadirkan jendela konteks 1.0M, kecepatan 240 token/detik, indeks kecerdasan 69/100, se

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

Model Unggulan Garis Depan

Grok 4.6 dari xAI adalah model AI yang dirancang untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, menghadirkan jendela konteks 500K, kecepatan 115 token/detik, indeks kecerdasan 77/100, serta tarif $2/$6

Intelligence Index77 / 100
Input Token:

$2.00 / 1M

Output Token:

$6.00 / 1M

Context Window:

500K tokens

SWE-bench Verified:

75.8%

Perbedaan Utama Sekilas

  • 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

Siapa Unggul di Mana?

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

Skor Benchmark Terverifikasi

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

Latensi & Waktu Respons

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

Simulator Biaya Token Interaktif

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

  • Jendela konteks 1.0M dengan kapasitas output maksimum hingga 33K token (MMLU-Pro 81.2% · Arena Elo 1375)
  • Kecepatan generasi tinggi 240 token/detik dengan latensi token pertama (TTFT) hanya 290 ms
  • Skema harga API kompetitif (tarif $0.4/$2 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur
Considerations & Trade-offs
  • Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
  • Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Grok 4.6 · Strengths & Key Advantages

  • Jendela konteks 500K dengan kapasitas output maksimum hingga 66K token (SWE-bench Verified 75.8% · GPQA Diamond 77.2%)
  • Kecepatan generasi tinggi 115 token/detik dengan latensi token pertama (TTFT) hanya 580 ms
  • Skema harga API kompetitif (tarif $2/$6 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur
Considerations & Trade-offs
  • Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
  • Untuk trafik produksi skala besar, disarankan mengaktifkan fitur Prompt Caching dan Batch API guna menghemat biaya

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

Spesifikasi Teknis

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: Jawaban Cepat (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.

Kesimpulan

Baik Gemini 3.5 Flash-Lite (Sangat cocok untuk interaksi real-time dengan latensi sangat rendah dan routing bervolume tinggi, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Grok 4.6 (Sangat cocok untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Grok 4.6 unggul dalam penalaran dan pemrograman, Gemini 3.5 Flash-Lite memberikan kecepatan generasi token lebih tinggi, dan Gemini 3.5 Flash-Lite lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Gemini 3.5 Flash-Lite atau Grok 4.6?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Grok 4.6 memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Gemini 3.5 Flash-Lite vs Grok 4.6?+

Gemini 3.5 Flash-Lite mengenakan biaya $0.40 / $2.00 per 1M token input/output, sedangkan Grok 4.6 sebesar $2.00 / $6.00. Secara keseluruhan, Gemini 3.5 Flash-Lite lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

Gemini 3.5 Flash-Lite mendukung konteks 1.05M token dibandingkan 500K token pada Grok 4.6, sementara Gemini 3.5 Flash-Lite unggul dalam kecepatan output token per detik.

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.

Sumber resmi: Dokumentasi API resmi Google & xAI serta evaluasi benchmark independen (2026).