Nano Banana vs GPT Image 1.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Nano Banana by Google and GPT Image 1.5 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, Nano Banana holds a notable pricing advantage for token ingestion. On output generation, Nano Banana is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Nano Banana provides an expanded memory horizon with 33K tokens. In latency-sensitive production environments, Nano Banana 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.
किसी भी मॉडल को बदलें और तुलना करें
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Nano Banana
डीप रीजनिंग मॉडलGoogle द्वारा विकसित Nano Banana एक उन्नत AI मॉडल है, जिसमें 32K टोकन का कॉन्टेक्स्ट विंडो और $2/1M इनपुट तथा $20/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।
$2.00 / 1M
$20.00 / 1M
32.8K tokens
N/A
GPT Image 1.5
फ्लैगशिप फ्रंटियर मॉडलOpenAI द्वारा विकसित GPT Image 1.5 एक उन्नत AI मॉडल है, जिसमें 4K टोकन का कॉन्टेक्स्ट विंडो और $5/1M इनपुट तथा $40/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।
$5.00 / 1M
$40.00 / 1M
4.1K tokens
52%
एक नज़र में मुख्य अंतर
- Nano Banana is 2.0x cheaper for API usage (input + output tokens combined).
- Nano Banana has a larger context window (33K vs 4K tokens).
- Nano Banana is faster at 60 tok/s vs 17 tok/s.
| Feature | Nano Banana | GPT Image 1.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 75 | 46 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $2.00 / 1M | $5.00 / 1M |
Output Price Cost per million output tokens | $20.00 / 1M | $40.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $2.75 / 1M | $5.00 / 1M |
Context Window Maximum input + output tokens | 32.8K tokens~49 A4 pages of text | 4.1K tokens~6 A4 pages of text |
Max Output Tokens Per single request | 8K | 4K |
Throughput Speed Tokens generated per second | 60 tok/s | 17 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 52% |
GPQA Diamond Graduate-level reasoning | N/A | 66% |
Cached Input Price Prompt caching discount per 1M tokens | $0.50 / 1M | $0.00 / 1M |
किस क्षेत्र में कौन आगे है?
At-a-glance decision frameworkEvaluations ongoing
सत्यापित बेंचमार्क स्कोर तुलना
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.
लेटेंसी और रिस्पॉन्स समय
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
रियल-टाइम टोकन लागत सिम्युलेटर
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Nano Banana · Strengths & Key Advantages
- Google की उन्नत Conversational in-context image generation and editing transformer आर्किटेक्चर और 32K कॉन्टेक्स्ट विंडो क्षमता
- किफायती और पारदर्शी API मूल्य ($2/1M इनपुट, $20/1M आउटपुट) तेज़ रिस्पॉन्स के साथ
- जटिल कोडिंग, तार्किक विश्लेषण और बहुभाषी कार्यों में उच्च सटीकता
Considerations & Trade-offs
- बड़े पैमाने पर लंबे कॉन्टेक्स्ट प्रॉम्प्ट्स के लिए टोकन बजट प्रबंधन आवश्यक है
- विशिष्ट डोमेन कार्यों में सर्वोत्तम परिणाम के लिए सटीक प्रॉम्प्ट इंजीनियरिंग की आवश्यकता होती है
GPT Image 1.5 · Strengths & Key Advantages
- OpenAI की उन्नत Native multimodal image generation transformer आर्किटेक्चर और 4K कॉन्टेक्स्ट विंडो क्षमता
- किफायती और पारदर्शी API मूल्य ($5/1M इनपुट, $40/1M आउटपुट) तेज़ रिस्पॉन्स के साथ
- जटिल कोडिंग, तार्किक विश्लेषण और बहुभाषी कार्यों में उच्च सटीकता
Considerations & Trade-offs
- बड़े पैमाने पर लंबे कॉन्टेक्स्ट प्रॉम्प्ट्स के लिए टोकन बजट प्रबंधन आवश्यक है
- विशिष्ट डोमेन कार्यों में सर्वोत्तम परिणाम के लिए सटीक प्रॉम्प्ट इंजीनियरिंग की आवश्यकता होती है
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Nano Banana | GPT Image 1.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.03 | $0.34 |
Output Tokens / Task Tokens generated per task | 1K | 9K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
तकनीकी विशिष्टताएँ (Specs)
Architecture, release timeline, capabilities, and modalities.
| Feature | Nano Banana | GPT Image 1.5 |
|---|---|---|
| Official API Model ID (SDK) | models/nano-banana | gpt-image-1-5 |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-08 | 2025-11 |
| Knowledge Cutoff | June 2025 | October 2025 |
| Architecture | Conversational in-context image generation and editing transformer | Native multimodal image generation transformer |
| 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 ($2/1M in) across full 33K window | Flat rate ($5/1M in) across full 4K window |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0.5/1M with configurable TTL | Automatic prefix caching at $0/1M (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 | Prompt-based JSON extraction only |
| 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) | 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 | text, vision |
| Output Modalities | image | image |
Nano Banana vs GPT Image 1.5: त्वरित प्रश्नोत्तर (FAQ)
Which is more intelligent, Nano Banana or GPT Image 1.5?
Nano Banana is more intelligent. It scores 75 compared with 46 on the composite Intelligence Index.
Which model is faster?
Nano Banana is faster, generating 60 tokens per second compared with 17 tokens per second.
Which model is cheaper to run?
Nano Banana is cheaper at $2.75 per 1M tokens blended, compared with $5.00 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Nano Banana has lower latency, with a time to first token of 0.90s compared with 7.10s.
Which model has a larger context window?
Nano Banana has a larger context window, supporting 32.8K tokens compared with 4.1K tokens.
निष्कर्ष
Verdict & Model Selection Recommendation: Choose Nano Banana if your priority is Storyboarding, consistent character illustration, and interactive image tweaking. Its performance in demanding tasks justifies its operational token cost. Choose GPT Image 1.5 if your workflow prioritizes Design and marketing workflows needing reliable prompt adherence. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
अक्सर पूछे जाने वाले प्रश्न (FAQ)
How does Nano Banana compare to GPT Image 1.5 in API token pricing and cost per token?+
Nano Banana costs $2.00 per 1M input tokens and $20.00 per 1M output tokens, whereas GPT Image 1.5 costs $5.00 input and $40.00 output. Overall, Nano Banana 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: Nano Banana or GPT Image 1.5?+
Nano Banana supports a context window of 33K tokens (~25k words), while GPT Image 1.5 supports 4K tokens (~3k words). Therefore, Nano Banana can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Nano Banana or GPT Image 1.5?+
Nano Banana scores at the frontier of coding benchmarks, while GPT Image 1.5 scores 52%. 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 Nano Banana and GPT Image 1.5?+
Nano Banana features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GPT Image 1.5 focuses on standard fast inference.
How does inference speed (tokens per second) compare between Nano Banana and GPT Image 1.5?+
Nano Banana generates approximately 60 tokens per second, while GPT Image 1.5 delivers 17 tokens per second. In latency-sensitive production environments, Nano Banana provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Nano Banana and GPT Image 1.5 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Nano Banana 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.
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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.
स्रोत: GetInfoUs Independent AI Benchmarks & Official API Specs