GPT Image 1.5 vs Nano Banana Pro
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GPT Image 1.5 by OpenAI and Nano Banana Pro 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 Pro holds a notable pricing advantage for token ingestion. On output generation, Nano Banana Pro is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Nano Banana Pro provides an expanded memory horizon with 66K tokens. In latency-sensitive production environments, Nano Banana Pro 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.
بدّل وقارن بين أي نموذجين
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
GPT Image 1.5
نموذج رائد متقدمنموذج GPT Image 1.5 المقدم من OpenAI يوفر نافذة سياق تصل إلى 4K رمز، بتكلفة $5 لكل مليون رمز إدخال و$40 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$5.00 / 1M
$40.00 / 1M
4.1K tokens
52%
Nano Banana Pro
نموذج استدلال عميقنموذج Nano Banana Pro المقدم من Google يوفر نافذة سياق تصل إلى 65K رمز، بتكلفة $3.5 لكل مليون رمز إدخال و$35 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$3.50 / 1M
$35.00 / 1M
65.5K tokens
N/A
أهم الفروقات في لمحة
- Nano Banana Pro is 1.1x cheaper for API usage (input + output tokens combined).
- Nano Banana Pro has a larger context window (66K vs 4K tokens).
- Nano Banana Pro is faster at 50 tok/s vs 17 tok/s.
| Feature | GPT Image 1.5 | Nano Banana Pro |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 46 | 83 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $5.00 / 1M | $3.50 / 1M |
Output Price Cost per million output tokens | $40.00 / 1M | $35.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $5.00 / 1M | $4.76 / 1M |
Context Window Maximum input + output tokens | 4.1K tokens~6 A4 pages of text | 65.5K tokens~98 A4 pages of text |
Max Output Tokens Per single request | 4K | 16K |
Throughput Speed Tokens generated per second | 17 tok/s | 50 tok/s |
SWE-bench Verified Real-world software engineering | 52% | N/A |
GPQA Diamond Graduate-level reasoning | 66% | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.80 / 1M |
من يتفوق في كل مجال؟
At-a-glance decision frameworkEvaluations ongoing
نتائج الاختبارات المعيارية الموثقة
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.
زمن الاستجابة والسرعة
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
محاكي تكلفة الرموز التفاعلي
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT Image 1.5 · Strengths & Key Advantages
- بنية Native multimodal image generation transformer متطورة من OpenAI مع سعة سياق تبلغ 4K رمز
- كفاءة عالية في التكلفة ($5/1M إدخال و$40/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
Nano Banana Pro · Strengths & Key Advantages
- بنية High-fidelity design-grade image generation and compositional editor متطورة من Google مع سعة سياق تبلغ 65K رمز
- كفاءة عالية في التكلفة ($3.5/1M إدخال و$35/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT Image 1.5 | Nano Banana Pro |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.34 | $0.05 |
Output Tokens / Task Tokens generated per task | 9K | 2K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 1K |
المواصفات التقنية
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT Image 1.5 | Nano Banana Pro |
|---|---|---|
| Official API Model ID (SDK) | gpt-image-1-5 | models/nano-banana-pro |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2025-11 | 2025-11 |
| Knowledge Cutoff | October 2025 | September 2025 |
| Architecture | Native multimodal image generation transformer | High-fidelity design-grade image generation and compositional editor |
| 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 ($3.5/1M in) across full 66K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0/1M (no extra cache-write fee) | Implicit + Explicit Context Caching at $0.8/1M 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 1.5 vs Nano Banana Pro: إجابات سريعة (FAQ)
Which is more intelligent, GPT Image 1.5 or Nano Banana Pro?
Nano Banana Pro is more intelligent. It scores 83 compared with 46 on the composite Intelligence Index.
Which model is faster?
Nano Banana Pro is faster, generating 50 tokens per second compared with 17 tokens per second.
Which model is cheaper to run?
Nano Banana Pro is cheaper at $4.76 per 1M tokens blended, compared with $5.00 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Nano Banana Pro has lower latency, with a time to first token of 1.20s compared with 7.10s.
Which model has a larger context window?
Nano Banana Pro has a larger context window, supporting 65.5K tokens compared with 4.1K tokens.
الحكم النهائي
Verdict & Model Selection Recommendation: Choose GPT Image 1.5 if your priority is Design and marketing workflows needing reliable prompt adherence. Its performance in demanding tasks justifies its operational token cost. Choose Nano Banana Pro if your workflow prioritizes Brand kit production, professional UI asset generation, and commercial marketing campaigns. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
الأسئلة الشائعة (FAQ)
How does GPT Image 1.5 compare to Nano Banana Pro in API token pricing and cost per token?+
GPT Image 1.5 costs $5.00 per 1M input tokens and $40.00 per 1M output tokens, whereas Nano Banana Pro costs $3.50 input and $35.00 output. Overall, Nano Banana Pro 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 1.5 or Nano Banana Pro?+
GPT Image 1.5 supports a context window of 4K tokens (~3k words), while Nano Banana Pro supports 66K tokens (~49k words). Therefore, Nano Banana Pro can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GPT Image 1.5 or Nano Banana Pro?+
GPT Image 1.5 scores 52% on SWE-bench Verified, while Nano Banana Pro scores competitively for software generation. For complex autonomous debugging and agentic engineering, GPT Image 1.5 provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between GPT Image 1.5 and Nano Banana Pro?+
GPT Image 1.5 is optimized for low-latency direct generation. In contrast, Nano Banana Pro provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GPT Image 1.5 and Nano Banana Pro?+
GPT Image 1.5 generates approximately 17 tokens per second, while Nano Banana Pro delivers 50 tokens per second. In latency-sensitive production environments, Nano Banana Pro provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT Image 1.5 and Nano Banana Pro to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Nano Banana Pro to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT Image 1.5. 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.
Related reading
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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.
المصادر الرسمية: GetInfoUs Independent AI Benchmarks & Official API Specs