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Nano Banana vs GPT Image 1.5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Nano Banana vs GPT Image 1.5: Benchmarks, Pricing & Speed Comparison

Nano Banana von Google und GPT Image 1.5 von OpenAI gehören zu den führenden KI-Modellen. Nano Banana bietet ein Kontextfenster von 32.8K Tokens bei API-Preisen von $2.00/$20.00 pro 1M Tokens, während GPT Image 1.5 4.1K Tokens Kontext zu $5.00/$40.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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Popular Matchups:
Google

Nano Banana

KI-Bildgenerierungsmodell

Nano Banana von Google ist ein KI-Modell für hochauflösende KI-Bildgenerierung, visuelles Design und präzises Text-Rendering mit 33K Kontextfenster, 60 Token/s Durchsatz, einem Intelligenz-Index von 75/100 und $2/$20 pro 1M Token.

Intelligence Index75 / 100
Input Token:

$2.00 / 1M

Output Token:

$20.00 / 1M

Context Window:

32.8K tokens

SWE-bench Verified:

N/A

OpenAI

GPT Image 1.5

KI-Bildgenerierungsmodell

GPT Image 1.5 von OpenAI ist ein KI-Modell für hochauflösende KI-Bildgenerierung, visuelles Design und präzises Text-Rendering mit 4K Kontextfenster, 17 Token/s Durchsatz, einem Intelligenz-Index von 46/100 und $5/$40 pro 1M Token.

Intelligence Index46 / 100
Input Token:

$5.00 / 1M

Output Token:

$40.00 / 1M

Context Window:

4.1K tokens

SWE-bench Verified:

52%

Wichtigste Unterschiede auf einen Blick

  • 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.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureNano BananaGPT Image 1.5
Intelligence Index
Composite reasoning & knowledge score (0-100)
7546
Provider
GoogleOpenAI
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 text4.1K tokens~6 A4 pages of text
Max Output Tokens
Per single request
8K4K
Throughput Speed
Tokens generated per second
60 tok/s17 tok/s
SWE-bench Verified
Real-world software engineering
N/A52%
GPQA Diamond
Graduate-level reasoning
N/A66%
Cached Input Price
Prompt caching discount per 1M tokens
$0.50 / 1M$0.00 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Nano Banana
2.0x cheaper monthly runtime
Speed
Nano Banana
+253% faster generation
Context
Nano Banana
32.8K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Nano BananaGPT Image 1.5
SWE-bench Verified (Autonomous Coding)

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

N/Avs52%
GPQA Diamond (Doctorate-Level Science & Math)

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

N/Avs66%
MMLU-Pro (Multi-task Language Understanding)

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

N/Avs78.5%
LMSYS Chatbot Arena Elo

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

1360ptsvs1315pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs9.2%
Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs30.5%
SciCode (Scientific Computing)

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

N/Avs32%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs56.5%
AIME (Advanced Math Competition)

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

N/Avs84%
Long-Context Reasoning

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

N/Avs68.5%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

60 tok/s vs17 tok/s

Latenz & Antwortzeit

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
Nano Banana0.90s
GPT Image 1.57.10s
Est. End-to-End Response (500 tokens)TTFT + generation time
Nano Banana9.23s
GPT Image 1.536.51s

Interaktiver Token-Kosten-Simulator

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

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Nano Banana Monthly API Bill
$21.75 / month
$2/1M in · $20/1M out
GPT Image 1.5 Monthly API Bill
$43.50 / month
$5/1M in · $40/1M out
Nano Banana saves approximately $21.75 / month (50%) under this workload.2.0x Savings

Nano Banana · Strengths & Key Advantages

  • 33K Kontextfenster und bis zu 8K maximale Output-Token (Arena Elo 1360)
  • Hohe Generierungsgeschwindigkeit von 60 Token/Sekunde bei nur 900 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($2/$20 pro 1M Token) inklusive Prompt-Caching, Tool-Calling und strikter JSON-Ausgabe
Considerations & Trade-offs
  • Proprietäres Cloud-Modell ohne Möglichkeit zum lokalen Download der Modellgewichte
  • Bei hohem Anfragevolumen empfiehlt sich der Einsatz von Prompt-Caching und Batch-API zur Kostenoptimierung

GPT Image 1.5 · Strengths & Key Advantages

  • 4K Kontextfenster und bis zu 4K maximale Output-Token (SWE-bench Verified 52% · GPQA Diamond 66%)
  • Hohe Generierungsgeschwindigkeit von 17 Token/Sekunde bei nur 7100 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($5/$40 pro 1M Token) inklusive Prompt-Caching, Tool-Calling und strikter JSON-Ausgabe
Considerations & Trade-offs
  • Proprietäres Cloud-Modell ohne Möglichkeit zum lokalen Download der Modellgewichte
  • Bei hohem Anfragevolumen empfiehlt sich der Einsatz von Prompt-Caching und Batch-API zur Kostenoptimierung

Token Use & Task Economics

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

MetricNano BananaGPT Image 1.5
Cost per Task
Estimated USD to finish one task
$0.03$0.34
Output Tokens / Task
Tokens generated per task
1K9K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureNano BananaGPT Image 1.5
Official API Model ID (SDK)models/nano-bananagpt-image-1-5
API ProtocolGoogle Gemini API (generateContent) + OpenAI-Compatible SDKOpenAI Responses API & Chat Completions v1
Release Date2025-082025-11
Knowledge CutoffJune 2025October 2025
ArchitectureConversational in-context image generation and editing transformerNative multimodal image generation transformer
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM10,000 – 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 windowFlat rate ($5/1M in) across full 4K window
Prompt Cache Policy & TTLImplicit + Explicit Context Caching at $0.5/1M with configurable TTLAutomatic prefix caching at $0/1M (no extra cache-write fee)
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaPrompt-based JSON extraction only
Web Search Grounding & SandboxBuilt-in Google Search Grounding + Managed Python Code Execution SandboxNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportVertex AI Supervised Adapter Tuning & Context DistillationSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token BillingText 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 VRAMCloud-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 ClassificationGoogle DeepMind Frontier Safety Framework VerifiedOpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise TierSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestext, visiontext, vision
Output Modalitiesimageimage

Nano Banana vs GPT Image 1.5: Schnelle Antworten (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.

Fazit

Sowohl Nano Banana (Optimal für hochauflösende KI-Bildgenerierung, visuelles Design und präzises Text-Rendering, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch GPT Image 1.5 (Optimal für hochauflösende KI-Bildgenerierung, visuelles Design und präzises Text-Rendering, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Nano Banana führt bei komplexem Reasoning und Coding, Nano Banana liefert den höheren Token-Durchsatz und Nano Banana ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Nano Banana oder GPT Image 1.5?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Nano Banana vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Nano Banana und GPT Image 1.5?+

Nano Banana kostet $2.00 / $20.00 pro 1M Input-/Output-Tokens, GPT Image 1.5 liegt bei $5.00 / $40.00. Beim gewichteten Gesamtpreis ist Nano Banana günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

Nano Banana bietet 32.8K Tokens Kontext gegenüber 4.1K Tokens bei GPT Image 1.5. Bei der Generierungsgeschwindigkeit liegt Nano Banana vorn.

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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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.

Offizielle Quellen: Offizielle API-Dokumentation von Google und OpenAI, Systemkarten und unabhängige Benchmark-Auswertungen (2026).