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Head-to-Head Model Comparison

GPT Image 1 vs Imagen 4

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 vs Imagen 4: Benchmarks, Pricing & Speed Comparison

GPT Image 1 von OpenAI und Imagen 4 von Google gehören zu den führenden KI-Modellen. GPT Image 1 bietet ein Kontextfenster von 4.1K Tokens bei API-Preisen von $5.00/$40.00 pro 1M Tokens, während Imagen 4 2K Tokens Kontext zu $40.00/$0.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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OpenAI

GPT Image 1

KI-Bildgenerierungsmodell

GPT Image 1 von OpenAI ist ein KI-Modell für hochauflösende KI-Bildgenerierung, visuelles Design und präzises Text-Rendering mit 4K Kontextfenster, 14 Token/s Durchsatz, einem Intelligenz-Index von 41/100 und $5/$40 pro 1M Token.

Intelligence Index41 / 100
Input Token:

$5.00 / 1M

Output Token:

$40.00 / 1M

Context Window:

4.1K tokens

SWE-bench Verified:

38.8%

Google

Imagen 4

KI-Bildgenerierungsmodell

Imagen 4 von Google ist ein KI-Modell für hochauflösende KI-Bildgenerierung, visuelles Design und präzises Text-Rendering mit 2K Kontextfenster, 25 Token/s Durchsatz, einem Intelligenz-Index von 82/100 und $40/$0 pro 1M Token.

Intelligence Index82 / 100
Input Token:

$40.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

2K tokens

SWE-bench Verified:

N/A

Wichtigste Unterschiede auf einen Blick

  • GPT Image 1 is 1.4x cheaper for API usage (input + output tokens combined).
  • GPT Image 1 has a larger context window (4K vs 2K tokens).
  • Imagen 4 is faster at 25 tok/s vs 14 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGPT Image 1Imagen 4
Intelligence Index
Composite reasoning & knowledge score (0-100)
4182
Provider
OpenAIGoogle
Input Price
Cost per million input tokens
$5.00 / 1M$40.00 / 1M
Output Price
Cost per million output tokens
$40.00 / 1M$0.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$5.00 / 1M$8.00 / 1M
Context Window
Maximum input + output tokens
4.1K tokens~6 A4 pages of text2K tokens~3 A4 pages of text
Max Output Tokens
Per single request
4K4K
Throughput Speed
Tokens generated per second
14 tok/s25 tok/s
SWE-bench Verified
Real-world software engineering
38.8%N/A
GPQA Diamond
Graduate-level reasoning
53.6%N/A

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
GPT Image 1
1.4x cheaper monthly runtime
Speed
Imagen 4
+79% faster generation
Context
GPT Image 1
4.1K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

GPT Image 1Imagen 4
SWE-bench Verified (Autonomous Coding)

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

38.8%vsN/A
GPQA Diamond (Doctorate-Level Science & Math)

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

53.6%vsN/A
MMLU-Pro (Multi-task Language Understanding)

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

72.6%vsN/A
LMSYS Chatbot Arena Elo

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

1286ptsvs1390pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

4.1%vsN/A
Terminal-Bench (Agentic Coding & Terminal Use)

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

18.5%vsN/A
SciCode (Scientific Computing)

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

27.3%vsN/A
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

43.9%vsN/A
AIME (Advanced Math Competition)

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

29.7%vsN/A
Long-Context Reasoning

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

58.2%vsN/A
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

14 tok/s vs25 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 18.20s
Imagen 42.00s
Est. End-to-End Response (500 tokens)TTFT + generation time
GPT Image 143.91s
Imagen 422.00s

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 Monthly API Bill
$43.50 / month
$5/1M in · $40/1M out
Imagen 4 Monthly API Bill
$60.00 / month
$40/1M in · $0/1M out
GPT Image 1 saves approximately $16.50 / month (28%) under this workload.1.4x Savings

GPT Image 1 · Strengths & Key Advantages

  • 4K Kontextfenster und bis zu 4K maximale Output-Token (SWE-bench Verified 38.8% · GPQA Diamond 53.6%)
  • Hohe Generierungsgeschwindigkeit von 14 Token/Sekunde bei nur 8200 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

Imagen 4 · Strengths & Key Advantages

  • 2K Kontextfenster und bis zu 4K maximale Output-Token (Arena Elo 1390)
  • Hohe Generierungsgeschwindigkeit von 25 Token/Sekunde bei nur 2000 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($40/$0 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 1Imagen 4
Cost per Task
Estimated USD to finish one task
$0.32$0.04
Output Tokens / Task
Tokens generated per task
8K0K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureGPT Image 1Imagen 4
Official API Model ID (SDK)gpt-image-1models/imagen-4
API ProtocolOpenAI Responses API & Chat Completions v1Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date2025-042025-05
Knowledge CutoffApril 2025March 2025
ArchitectureNative multimodal image generation transformerHigh-resolution multimodal image diffusion 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 ($5/1M in) across full 4K windowFlat rate ($40/1M in) across full 2K window
Prompt Cache Policy & TTLAutomatic prefix caching at $0/1M (no extra cache-write fee)Implicit + Explicit Context Caching at $0/1M with configurable TTL
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)Text-only completion (No GUI action loop)
MCP & Parallel Tool CallingPrompt-based JSON extraction onlyPrompt-based JSON extraction only
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)
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
Output Modalitiesimageimage

GPT Image 1 vs Imagen 4: Schnelle Antworten (FAQ)

Which is more intelligent, GPT Image 1 or Imagen 4?

Imagen 4 is more intelligent. It scores 82 compared with 41 on the composite Intelligence Index.

Which model is faster?

Imagen 4 is faster, generating 25 tokens per second compared with 14 tokens per second.

Which model is cheaper to run?

GPT Image 1 is cheaper at $5.00 per 1M tokens blended, compared with $8.00 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Imagen 4 has lower latency, with a time to first token of 2.00s compared with 8.20s.

Which model has a larger context window?

GPT Image 1 has a larger context window, supporting 4.1K tokens compared with 2K tokens.

Fazit

Sowohl GPT Image 1 (Optimal für hochauflösende KI-Bildgenerierung, visuelles Design und präzises Text-Rendering, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Imagen 4 (Optimal für hochauflösende KI-Bildgenerierung, visuelles Design und präzises Text-Rendering, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Imagen 4 führt bei komplexem Reasoning und Coding, Imagen 4 liefert den höheren Token-Durchsatz und GPT Image 1 ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – GPT Image 1 oder Imagen 4?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Imagen 4 vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von GPT Image 1 und Imagen 4?+

GPT Image 1 kostet $5.00 / $40.00 pro 1M Input-/Output-Tokens, Imagen 4 liegt bei $40.00 / $0.00. Beim gewichteten Gesamtpreis ist GPT Image 1 günstiger.

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

GPT Image 1 bietet 4.1K Tokens Kontext gegenüber 2K Tokens bei Imagen 4. Bei der Generierungsgeschwindigkeit liegt Imagen 4 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).