Head-to-Head Model Comparison

Gemini 2.0 Flash vs GPT-4o mini

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

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Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Gemini 2.0 FlashProfile

GPT-4o miniProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Google

Gemini 2.0 Flash

Inferensi Super Cepat

Gemini 2.0 Flash 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 40/100, serta t

Intelligence Index40 / 100

Input Token:

$0.10 / 1M

Output Token:

$0.40 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

42%

OpenAI

GPT-4o mini

Model Ringan & Hemat Biaya

GPT-4o mini dari OpenAI adalah model AI yang dirancang untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, menghadirkan jendela konteks 128K, kecepatan 140 token/detik, indeks kecerdasan 30/100, serta tarif $0.

Intelligence Index30 / 100

Input Token:

$0.15 / 1M

Output Token:

$0.60 / 1M

Context Window:

128K tokens

SWE-bench Verified:

24.5%

Perbedaan Utama Sekilas

Gemini 2.0 Flash is 1.6x cheaper for API usage (input + output tokens combined).
Gemini 2.0 Flash has a larger context window (1M vs 128K tokens).
Gemini 2.0 Flash is faster at 240 tok/s vs 140 tok/s.
Gemini 2.0 Flash leads on SWE-bench coding (42.0% vs 24.5%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	Gemini 2.0 Flash	GPT-4o mini
Intelligence Index Composite reasoning & knowledge score (0-100)	40	30
Provider	Google	OpenAI
Input Price Cost per million input tokens	$0.10 / 1M	$0.15 / 1M
Output Price Cost per million output tokens	$0.40 / 1M	$0.60 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.08 / 1M	$0.14 / 1M
Context Window Maximum input + output tokens	1.05M tokens~1,573 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	8K	16K
Throughput Speed Tokens generated per second	240 tok/s	140 tok/s
SWE-bench Verified Real-world software engineering	42%	24.5%
GPQA Diamond Graduate-level reasoning	55.4%	40.2%
Cached Input Price Prompt caching discount per 1M tokens	$0.03 / 1M	$0.07 / 1M

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

Gemini 2.0 Flash

+17.5% SWE-bench lead

Cost

Gemini 2.0 Flash

1.6x cheaper monthly runtime

Speed

Gemini 2.0 Flash

+71% faster generation

Context

Gemini 2.0 Flash

1.05M tokens window

Skor Benchmark Terverifikasi

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

Gemini 2.0 FlashGPT-4o mini

SWE-bench Verified (Autonomous Coding)

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

42%vs24.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

55.4%vs40.2%

MMLU-Pro (Multi-task Language Understanding)

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

71%vs58.5%

LMSYS Chatbot Arena Elo

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

1302ptsvs1228pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

8.9%vs6.4%

Terminal-Bench (Agentic Coding & Terminal Use)

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

27.7%vs16.2%

SciCode (Scientific Computing)

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

25.2%vs14.7%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

44.9%vs28.3%

AIME (Advanced Math Competition)

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

63.7%vs46.2%

Long-Context Reasoning

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

63.9%vs52.7%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

240 tok/s vs140 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 2.0 Flash0.28s

GPT-4o mini0.31s

Est. End-to-End Response (500 tokens)TTFT + generation time

Gemini 2.0 Flash2.36s

GPT-4o mini3.88s

Simulator Biaya Token Interaktif

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

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Gemini 2.0 Flash Monthly API Bill

$0.55 / month

$0.1/1M in · $0.4/1M out

GPT-4o mini Monthly API Bill

$0.88 / month

$0.15/1M in · $0.6/1M out

Gemini 2.0 Flash saves approximately $0.33 / month (38%) under this workload.1.6x Savings

Gemini 2.0 Flash · Strengths & Key Advantages

Jendela konteks 1.0M dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 42% · GPQA Diamond 55.4%)
Kecepatan generasi tinggi 240 token/detik dengan latensi token pertama (TTFT) hanya 280 ms
Skema harga API kompetitif (tarif $0.1/$0.4 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

GPT-4o mini · Strengths & Key Advantages

Jendela konteks 128K dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 24.5% · GPQA Diamond 40.2%)
Kecepatan generasi tinggi 140 token/detik dengan latensi token pertama (TTFT) hanya 310 ms
Skema harga API kompetitif (tarif $0.15/$0.6 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
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Token Use & Task Economics

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

Metric	Gemini 2.0 Flash	GPT-4o mini
Cost per Task Estimated USD to finish one task	$0.13	$0.16
Output Tokens / Task Tokens generated per task	60K	45K
Reasoning Tokens / Task Thinking tokens burned per task	33K	0K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Feature	Gemini 2.0 Flash	GPT-4o mini
Official API Model ID (SDK)	models/gemini-2-flash	gpt-4o-mini
API Protocol	Google Gemini API (generateContent) + OpenAI-Compatible SDK	OpenAI Responses API & Chat Completions v1
Release Date	2024-12	2024-07
Knowledge Cutoff	August 2024	October 2023
Architecture	Multimodal Flash MoE	Small Multimodal Dense
Parameters	Confidential	Confidential
Enterprise Rate Limits (RPM / TPM)	10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM	10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens	Flat rate ($0.15/1M in) across full 128K window
Prompt Cache Policy & TTL	Implicit + Explicit Context Caching at $0.025/1M with configurable TTL	Automatic prefix caching at $0.075/1M (no extra cache-write fee)
Computer Use / GUI Agent	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool Calling	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & Sandbox	Built-in Google Search Grounding + Managed Python Code Execution Sandbox	Native Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning Support	Vertex AI Supervised Adapter Tuning & Context Distillation	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token Billing	Text 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)
Quantization & Min GPU VRAM	Cloud-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 Classification	Google DeepMind Frontier Safety Framework Verified	OpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text, vision, audio, video	text, vision
Output Modalities	text	text

Gemini 2.0 Flash vs GPT-4o mini: Jawaban Cepat (FAQ)

Which is more intelligent, Gemini 2.0 Flash or GPT-4o mini?

Gemini 2.0 Flash is more intelligent. It scores 40 compared with 30 on the composite Intelligence Index.

Which model is faster?

Gemini 2.0 Flash is faster, generating 240 tokens per second compared with 140 tokens per second.

Which model is cheaper to run?

Gemini 2.0 Flash is cheaper at $0.08 per 1M tokens blended, compared with $0.14 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini 2.0 Flash has lower latency, with a time to first token of 0.28s compared with 0.31s.

Which model has a larger context window?

Gemini 2.0 Flash has a larger context window, supporting 1.05M tokens compared with 128K tokens.

Kesimpulan

Baik Gemini 2.0 Flash (Sangat cocok untuk interaksi real-time dengan latensi sangat rendah dan routing bervolume tinggi, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun GPT-4o mini (Sangat cocok untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Gemini 2.0 Flash unggul dalam penalaran dan pemrograman, Gemini 2.0 Flash memberikan kecepatan generasi token lebih tinggi, dan Gemini 2.0 Flash lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Gemini 2.0 Flash atau GPT-4o mini?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Gemini 2.0 Flash memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Gemini 2.0 Flash vs GPT-4o mini?+

Gemini 2.0 Flash mengenakan biaya $0.10 / $0.40 per 1M token input/output, sedangkan GPT-4o mini sebesar $0.15 / $0.60. Secara keseluruhan, Gemini 2.0 Flash lebih hemat biaya.

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

Gemini 2.0 Flash mendukung konteks 1.05M token dibandingkan 128K token pada GPT-4o mini, sementara Gemini 2.0 Flash 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 & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Gemini 2.0 Flash vs GPT-4o mini

Ganti & Bandingkan Model Apa Saja

Gemini 2.0 Flash

GPT-4o mini

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Gemini 2.0 Flash · Strengths & Key Advantages

Considerations & Trade-offs

GPT-4o mini · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Gemini 2.0 Flash vs GPT-4o mini: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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