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GPT Image 1.5 vs Nano Banana Pro

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
GPT Image 1.5 vs Nano Banana Pro: Benchmarks, Pricing & Speed Comparison

GPT Image 1.5 von OpenAI und Nano Banana Pro von Google gehören zu den führenden KI-Modellen. GPT Image 1.5 bietet ein Kontextfenster von 4.1K Tokens bei API-Preisen von $5.00/$40.00 pro 1M Tokens, während Nano Banana Pro 65.5K Tokens Kontext zu $3.50/$35.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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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%

Google

Nano Banana Pro

KI-Bildgenerierungsmodell

Nano Banana Pro von Google ist ein KI-Modell für hochauflösende KI-Bildgenerierung, visuelles Design und präzises Text-Rendering mit 66K Kontextfenster, 50 Token/s Durchsatz, einem Intelligenz-Index von 83/100 und $3.5/$35 pro 1M Token.

Intelligence Index83 / 100
Input Token:

$3.50 / 1M

Output Token:

$35.00 / 1M

Context Window:

65.5K tokens

SWE-bench Verified:

N/A

Wichtigste Unterschiede auf einen Blick

  • 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.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGPT Image 1.5Nano Banana Pro
Intelligence Index
Composite reasoning & knowledge score (0-100)
4683
Provider
OpenAIGoogle
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 text65.5K tokens~98 A4 pages of text
Max Output Tokens
Per single request
4K16K
Throughput Speed
Tokens generated per second
17 tok/s50 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

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Nano Banana Pro
1.1x cheaper monthly runtime
Speed
Nano Banana Pro
+194% faster generation
Context
Nano Banana Pro
65.5K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

GPT Image 1.5Nano Banana Pro
SWE-bench Verified (Autonomous Coding)

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

52%vsN/A
GPQA Diamond (Doctorate-Level Science & Math)

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

66%vsN/A
MMLU-Pro (Multi-task Language Understanding)

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

78.5%vsN/A
LMSYS Chatbot Arena Elo

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

1315ptsvs1415pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

9.2%vsN/A
Terminal-Bench (Agentic Coding & Terminal Use)

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

30.5%vsN/A
SciCode (Scientific Computing)

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

32%vsN/A
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

56.5%vsN/A
AIME (Advanced Math Competition)

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

84%vsN/A
Long-Context Reasoning

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

68.5%vsN/A
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

17 tok/s vs50 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
GPT Image 1.57.10s
Nano Banana Pro1.20s
Est. End-to-End Response (500 tokens)TTFT + generation time
GPT Image 1.536.51s
Nano Banana Pro11.20s

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)
GPT Image 1.5 Monthly API Bill
$43.50 / month
$5/1M in · $40/1M out
Nano Banana Pro Monthly API Bill
$37.95 / month
$3.5/1M in · $35/1M out
Nano Banana Pro saves approximately $5.55 / month (13%) under this workload.1.1x Savings

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

Nano Banana Pro · Strengths & Key Advantages

  • 66K Kontextfenster und bis zu 16K maximale Output-Token (Arena Elo 1415)
  • Hohe Generierungsgeschwindigkeit von 50 Token/Sekunde bei nur 1200 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($3.5/$35 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.

MetricGPT Image 1.5Nano Banana Pro
Cost per Task
Estimated USD to finish one task
$0.34$0.05
Output Tokens / Task
Tokens generated per task
9K2K
Reasoning Tokens / Task
Thinking tokens burned per task
0K1K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureGPT Image 1.5Nano Banana Pro
Official API Model ID (SDK)gpt-image-1-5models/nano-banana-pro
API ProtocolOpenAI Responses API & Chat Completions v1Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date2025-112025-11
Knowledge CutoffOctober 2025September 2025
ArchitectureNative multimodal image generation transformerHigh-fidelity design-grade image generation and compositional editor
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 ($5/1M in) across full 4K windowFlat rate ($3.5/1M in) across full 66K window
Prompt Cache Policy & TTLAutomatic prefix caching at $0/1M (no extra cache-write fee)Implicit + Explicit Context Caching at $0.8/1M with configurable TTL
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingPrompt-based JSON extraction onlyNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxNative Web Search tool + Hosted Code Interpreter & File Search RAGBuilt-in Google Search Grounding + Managed Python Code Execution Sandbox
Official Fine-Tuning SupportSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)Vertex AI Supervised Adapter Tuning & Context Distillation
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 ClassificationOpenAI Preparedness Framework Audited & System Card PublishedGoogle DeepMind Frontier Safety Framework Verified
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

GPT Image 1.5 vs Nano Banana Pro: Schnelle Antworten (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.

Fazit

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

Häufig gestellte Fragen (FAQ)

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

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

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

GPT Image 1.5 kostet $5.00 / $40.00 pro 1M Input-/Output-Tokens, Nano Banana Pro liegt bei $3.50 / $35.00. Beim gewichteten Gesamtpreis ist Nano Banana Pro günstiger.

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

GPT Image 1.5 bietet 4.1K Tokens Kontext gegenüber 65.5K Tokens bei Nano Banana Pro. Bei der Generierungsgeschwindigkeit liegt Nano Banana Pro 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 OpenAI und Google, Systemkarten und unabhängige Benchmark-Auswertungen (2026).