Gemini 2.0 Flash vs GPT-4o mini
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Gemini 2.0 Flash by Google and GPT-4o mini by OpenAI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Gemini 2.0 Flash holds a notable pricing advantage for token ingestion. On output generation, Gemini 2.0 Flash is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Gemini 2.0 Flash provides an expanded memory horizon with 1,049K tokens. In latency-sensitive production environments, Gemini 2.0 Flash leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Gemini 2.0 Flash
Model Flagship TerdepanGemini 2.0 Flash dari Google adalah model AI canggih dengan jendela konteks 1.0M token serta harga API $0.1/1M token input dan $0.4/1M token output untuk pemrograman, penalaran, dan otomasi bisnis.
$0.10 / 1M
$0.40 / 1M
1.05M tokens
42%
GPT-4o mini
Model AI Performa TinggiGPT-4o mini dari OpenAI adalah model AI canggih dengan jendela konteks 128K token serta harga API $0.15/1M token input dan $0.6/1M token output untuk pemrograman, penalaran, dan otomasi bisnis.
$0.15 / 1M
$0.60 / 1M
128K tokens
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%).
| Feature | Gemini 2.0 Flash | GPT-4o mini |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 40 | 30 |
Provider | 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 frameworkSkor Benchmark Terverifikasi
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latensi & Waktu Respons
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulator Biaya Token Interaktif
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemini 2.0 Flash · Strengths & Key Advantages
- Arsitektur Multimodal Flash MoE mutakhir dari Google dengan dukungan konteks 1.0M token
- Efisiensi biaya API yang kompetitif ($0.1/1M input, $0.4/1M output) dan respons cepat
- Performa andal dalam pembuatan kode, analisis logika, serta pemahaman multibahasa
Considerations & Trade-offs
- Penggunaan konteks berukuran penuh memerlukan pengelolaan anggaran token yang cermat
- Membutuhkan perancangan prompt yang tepat untuk kasus penggunaan industri spesifik
GPT-4o mini · Strengths & Key Advantages
- Arsitektur Small Multimodal Dense mutakhir dari OpenAI dengan dukungan konteks 128K token
- Efisiensi biaya API yang kompetitif ($0.15/1M input, $0.6/1M output) dan respons cepat
- Performa andal dalam pembuatan kode, analisis logika, serta pemahaman multibahasa
Considerations & Trade-offs
- Penggunaan konteks berukuran penuh memerlukan pengelolaan anggaran token yang cermat
- Membutuhkan perancangan prompt yang tepat untuk kasus penggunaan industri spesifik
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
Verdict & Model Selection Recommendation: Choose Gemini 2.0 Flash if your priority is High-volume consumer chatbots, automated content pipelines, and video/audio analysis. Its performance in demanding tasks justifies its operational token cost. Choose GPT-4o mini if your workflow prioritizes Data extraction, customer support routing, and lightweight web scraping. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Pertanyaan Umum (FAQ)
How does Gemini 2.0 Flash compare to GPT-4o mini in API token pricing and cost per token?+
Gemini 2.0 Flash costs $0.10 per 1M input tokens and $0.40 per 1M output tokens, whereas GPT-4o mini costs $0.15 input and $0.60 output. Overall, Gemini 2.0 Flash is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Gemini 2.0 Flash or GPT-4o mini?+
Gemini 2.0 Flash supports a context window of 1,049K tokens (~786k words), while GPT-4o mini supports 128K tokens (~96k words). Therefore, Gemini 2.0 Flash can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Gemini 2.0 Flash or GPT-4o mini?+
Gemini 2.0 Flash scores 42% on SWE-bench Verified while GPT-4o mini scores 24.5%. For autonomous software engineering and multi-file debugging, Gemini 2.0 Flash holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Gemini 2.0 Flash and GPT-4o mini?+
Gemini 2.0 Flash features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GPT-4o mini focuses on standard fast inference.
How does inference speed (tokens per second) compare between Gemini 2.0 Flash and GPT-4o mini?+
Gemini 2.0 Flash generates approximately 240 tokens per second, while GPT-4o mini delivers 140 tokens per second. In latency-sensitive production environments, Gemini 2.0 Flash provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Gemini 2.0 Flash and GPT-4o mini to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Gemini 2.0 Flash to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-4o mini. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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: GetInfoUs Independent AI Benchmarks & Official API Specs