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Head-to-Head Model Comparison

GPT Image 2 vs Nano Banana 2

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
GPT Image 2 vs Nano Banana 2: Benchmarks, Pricing & Speed Comparison

GPT Image 2 von OpenAI und Nano Banana 2 von Google gehören zu den führenden KI-Modellen. GPT Image 2 bietet ein Kontextfenster von 4.1K Tokens bei API-Preisen von $5.00/$40.00 pro 1M Tokens, während Nano Banana 2 131.1K Tokens Kontext zu $3.00/$30.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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OpenAI

GPT Image 2

KI-Bildgenerierungsmodell

GPT Image 2 von OpenAI ist ein KI-Modell für hochauflösende KI-Bildgenerierung, visuelles Design und präzises Text-Rendering mit 4K Kontextfenster, 21 Token/s Durchsatz, einem Intelligenz-Index von 59/100 und $5/$40 pro 1M Token.

Intelligence Index59 / 100
Input Token:

$5.00 / 1M

Output Token:

$40.00 / 1M

Context Window:

4.1K tokens

SWE-bench Verified:

68.5%

Google

Nano Banana 2

KI-Bildgenerierungsmodell

Nano Banana 2 von Google ist ein KI-Modell für hochauflösende KI-Bildgenerierung, visuelles Design und präzises Text-Rendering mit 131K Kontextfenster, 65 Token/s Durchsatz, einem Intelligenz-Index von 88/100 und $3/$30 pro 1M Token.

Intelligence Index88 / 100
Input Token:

$3.00 / 1M

Output Token:

$30.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

N/A

Wichtigste Unterschiede auf einen Blick

  • 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.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGPT Image 2Nano Banana 2
Intelligence Index
Composite reasoning & knowledge score (0-100)
5988
Provider
OpenAIGoogle
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 text131.1K tokens~197 A4 pages of text
Max Output Tokens
Per single request
4K16K
Throughput Speed
Tokens generated per second
21 tok/s65 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

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Nano Banana 2
1.3x cheaper monthly runtime
Speed
Nano Banana 2
+210% faster generation
Context
Nano Banana 2
131.1K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

GPT Image 2Nano Banana 2
SWE-bench Verified (Autonomous Coding)

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

68.5%vsN/A
GPQA Diamond (Doctorate-Level Science & Math)

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

72.5%vsN/A
MMLU-Pro (Multi-task Language Understanding)

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

84.8%vsN/A
LMSYS Chatbot Arena Elo

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

1375ptsvs1455pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

12.2%vsN/A
Terminal-Bench (Agentic Coding & Terminal Use)

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

41.5%vsN/A
SciCode (Scientific Computing)

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

40%vsN/A
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

67%vsN/A
AIME (Advanced Math Competition)

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

89.6%vsN/A
Long-Context Reasoning

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

78%vsN/A
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

21 tok/s vs65 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 26.20s
Nano Banana 20.95s
Est. End-to-End Response (500 tokens)TTFT + generation time
GPT Image 230.01s
Nano Banana 28.64s

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 2 Monthly API Bill
$43.50 / month
$5/1M in · $40/1M out
Nano Banana 2 Monthly API Bill
$32.63 / month
$3/1M in · $30/1M out
Nano Banana 2 saves approximately $10.88 / month (25%) under this workload.1.3x Savings

GPT Image 2 · Strengths & Key Advantages

  • 4K Kontextfenster und bis zu 4K maximale Output-Token (SWE-bench Verified 68.5% · GPQA Diamond 72.5%)
  • Hohe Generierungsgeschwindigkeit von 21 Token/Sekunde bei nur 6200 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 2 · Strengths & Key Advantages

  • 131K Kontextfenster und bis zu 16K maximale Output-Token (Arena Elo 1455)
  • Hohe Generierungsgeschwindigkeit von 65 Token/Sekunde bei nur 950 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($3/$30 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 2Nano Banana 2
Cost per Task
Estimated USD to finish one task
$0.36$0.04
Output Tokens / Task
Tokens generated per task
9K3K
Reasoning Tokens / Task
Thinking tokens burned per task
0K1K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureGPT Image 2Nano Banana 2
Official API Model ID (SDK)gpt-image-2models/nano-banana-2
API ProtocolOpenAI Responses API & Chat Completions v1Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date2026-042026-05
Knowledge CutoffMarch 2026February 2026
ArchitectureFrontier native multimodal image transformerFrontier conversational multimodal image engine
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 ($3/1M in) across full 131K window
Prompt Cache Policy & TTLAutomatic prefix caching at $0/1M (no extra cache-write fee)Implicit + Explicit Context Caching at $0.75/1M 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 2 vs Nano Banana 2: Schnelle Antworten (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.

Fazit

Sowohl GPT Image 2 (Optimal für hochauflösende KI-Bildgenerierung, visuelles Design und präzises Text-Rendering, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Nano Banana 2 (Optimal für hochauflösende KI-Bildgenerierung, visuelles Design und präzises Text-Rendering, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Nano Banana 2 führt bei komplexem Reasoning und Coding, Nano Banana 2 liefert den höheren Token-Durchsatz und Nano Banana 2 ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – GPT Image 2 oder Nano Banana 2?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Nano Banana 2 vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von GPT Image 2 und Nano Banana 2?+

GPT Image 2 kostet $5.00 / $40.00 pro 1M Input-/Output-Tokens, Nano Banana 2 liegt bei $3.00 / $30.00. Beim gewichteten Gesamtpreis ist Nano Banana 2 günstiger.

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

GPT Image 2 bietet 4.1K Tokens Kontext gegenüber 131.1K Tokens bei Nano Banana 2. Bei der Generierungsgeschwindigkeit liegt Nano Banana 2 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).