GPT Image 2 vs Nano Banana 2
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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GPT Image 2 by OpenAI and Nano Banana 2 by Google 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, Nano Banana 2 holds a notable pricing advantage for token ingestion. On output generation, Nano Banana 2 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Nano Banana 2 provides an expanded memory horizon with 131K tokens. In latency-sensitive production environments, Nano Banana 2 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.
원하는 AI 모델 선택 및 실시간 비교
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GPT Image 2
플래그십 프론티어 모델OpenAI의 GPT Image 2 모델은 4K 토큰 컨텍스트 윈도우와 입력 $5/1M 토큰, 출력 $40/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$5.00 / 1M
$40.00 / 1M
4.1K tokens
68.5%
Nano Banana 2
심층 추론 모델Google의 Nano Banana 2 모델은 131K 토큰 컨텍스트 윈도우와 입력 $3/1M 토큰, 출력 $30/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$3.00 / 1M
$30.00 / 1M
131.1K tokens
N/A
한눈에 보는 핵심 차이점
- Nano Banana 2 is 1.3x cheaper for API usage (input + output tokens combined).
- Nano Banana 2 has a larger context window (131K vs 4K tokens).
- Nano Banana 2 is faster at 65 tok/s vs 21 tok/s.
| Feature | GPT Image 2 | Nano Banana 2 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 59 | 88 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $5.00 / 1M | $3.00 / 1M |
Output Price Cost per million output tokens | $40.00 / 1M | $30.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $5.00 / 1M | $4.13 / 1M |
Context Window Maximum input + output tokens | 4.1K tokens~6 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 4K | 16K |
Throughput Speed Tokens generated per second | 21 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 68.5% | N/A |
GPQA Diamond Graduate-level reasoning | 72.5% | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.75 / 1M |
부문별 우위 모델
At-a-glance decision frameworkEvaluations ongoing
검증된 벤치마크 점수 비교
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.
지연 시간(Latency) 및 응답 속도
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
실시간 API 토큰 비용 시뮬레이터
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT Image 2 · Strengths & Key Advantages
- OpenAI의 첨단 Frontier native multimodal image transformer 아키텍처와 최대 4K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $5/1M, 출력 $40/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Nano Banana 2 · Strengths & Key Advantages
- Google의 첨단 Frontier conversational multimodal image engine 아키텍처와 최대 131K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $3/1M, 출력 $30/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT Image 2 | Nano Banana 2 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.36 | $0.04 |
Output Tokens / Task Tokens generated per task | 9K | 3K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 1K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT Image 2 | Nano Banana 2 |
|---|---|---|
| Official API Model ID (SDK) | gpt-image-2 | models/nano-banana-2 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2026-04 | 2026-05 |
| Knowledge Cutoff | March 2026 | February 2026 |
| Architecture | Frontier native multimodal image transformer | Frontier conversational multimodal image engine |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($5/1M in) across full 4K window | Flat rate ($3/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0/1M (no extra cache-write fee) | Implicit + Explicit Context Caching at $0.75/1M with configurable TTL |
| 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 | Prompt-based JSON extraction only | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Vertex AI Supervised Adapter Tuning & Context Distillation |
| Multimodal Token Billing | Text 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 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 | OpenAI Preparedness Framework Audited & System Card Published | Google DeepMind Frontier Safety Framework Verified |
| 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 | text, vision |
| Output Modalities | image | image |
GPT Image 2 vs Nano Banana 2: 핵심 요약 문답 (FAQ)
Which is more intelligent, GPT Image 2 or Nano Banana 2?
Nano Banana 2 is more intelligent. It scores 88 compared with 59 on the composite Intelligence Index.
Which model is faster?
Nano Banana 2 is faster, generating 65 tokens per second compared with 21 tokens per second.
Which model is cheaper to run?
Nano Banana 2 is cheaper at $4.13 per 1M tokens blended, compared with $5.00 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Nano Banana 2 has lower latency, with a time to first token of 0.95s compared with 6.20s.
Which model has a larger context window?
Nano Banana 2 has a larger context window, supporting 131.1K tokens compared with 4.1K tokens.
종합 평가
Verdict & Model Selection Recommendation: Choose GPT Image 2 if your priority is High-end visual production and campaign assets. Its performance in demanding tasks justifies its operational token cost. Choose Nano Banana 2 if your workflow prioritizes Full-scale comic generation, commercial advertising visuals, and film concept art. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
자주 묻는 질문 (FAQ)
How does GPT Image 2 compare to Nano Banana 2 in API token pricing and cost per token?+
GPT Image 2 costs $5.00 per 1M input tokens and $40.00 per 1M output tokens, whereas Nano Banana 2 costs $3.00 input and $30.00 output. Overall, Nano Banana 2 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: GPT Image 2 or Nano Banana 2?+
GPT Image 2 supports a context window of 4K tokens (~3k words), while Nano Banana 2 supports 131K tokens (~98k words). Therefore, Nano Banana 2 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GPT Image 2 or Nano Banana 2?+
GPT Image 2 scores 68.5% on SWE-bench Verified, while Nano Banana 2 scores competitively for software generation. For complex autonomous debugging and agentic engineering, GPT Image 2 provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between GPT Image 2 and Nano Banana 2?+
GPT Image 2 is optimized for low-latency direct generation. In contrast, Nano Banana 2 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GPT Image 2 and Nano Banana 2?+
GPT Image 2 generates approximately 21 tokens per second, while Nano Banana 2 delivers 65 tokens per second. In latency-sensitive production environments, Nano Banana 2 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT Image 2 and Nano Banana 2 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Nano Banana 2 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT Image 2. 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.
공식 출처: GetInfoUs Independent AI Benchmarks & Official API Specs