Head-to-Head Model Comparison

GPT Image 2 vs GPT Image 1.5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Vérifié par l’équipe de recherche de GetInfoUsDernière révision : October 2026Politique éditoriale
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Related topics (12)
AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
GPT Image 2 vs GPT Image 1.5: Benchmarks, Pricing & Speed Comparison

GPT Image 2 by OpenAI 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, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 4K tokens. In latency-sensitive production environments, GPT Image 2 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.

Comparaison Face-à-Face (VS)

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OpenAI

GPT Image 2

Image de pointe

d'OpenAI 2026 image modèle pour prompt-driven génération, editing et design.

Intelligence Index59 / 100
Input Token:

$5.00 / 1M

Output Token:

$40.00 / 1M

Context Window:

4.1K tokens

SWE-bench Verified:

68.5%

OpenAI

GPT Image 1.5

Mise à niveau image

Improved prompt-driven génération, editing et visuel design flux de travail.

Intelligence Index46 / 100
Input Token:

$5.00 / 1M

Output Token:

$40.00 / 1M

Context Window:

4.1K tokens

SWE-bench Verified:

52%

Différences Clés en un Coup d’Œil

  • Both models cost the same under this workload — choose on capability, not price.
  • GPT Image 2 is faster at 21 tok/s vs 17 tok/s.
  • GPT Image 2 leads on SWE-bench coding (68.5% vs 52.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
CaractéristiqueGPT Image 2GPT Image 1.5
Intelligence Index
Composite reasoning & knowledge score (0-100)
5946
Provider
OpenAIOpenAI
Input Price
Cost per million input tokens
$5.00 / 1M$5.00 / 1M
Output Price
Cost per million output tokens
$40.00 / 1M$40.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$5.00 / 1M$5.00 / 1M
Context Window
Maximum input + output tokens
4.1K tokens~6 A4 pages of text4.1K tokens~6 A4 pages of text
Max Output Tokens
Per single request
4K4K
Throughput Speed
Tokens generated per second
21 tok/s17 tok/s
SWE-bench Verified
Real-world software engineering
68.5%52%
GPQA Diamond
Graduate-level reasoning
72.5%66%

Qui gagne où ?

At-a-glance decision framework
Code
GPT Image 2
+16.5% SWE-bench lead
Coût
Tied
Identical monthly runtime cost
Vitesse
GPT Image 2
+24% faster generation
Contexte
Tied
4.1K tokens window

Scores de Benchmarks Vérifiés

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

GPT Image 2GPT Image 1.5
SWE-bench Verified (Autonomous Coding)

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

68.5%vs52%
GPQA Diamond (Doctorate-Level Science & Math)

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

72.5%vs66%
MMLU-Pro (Multi-task Language Understanding)

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

84.8%vs78.5%
LMSYS Chatbot Arena Elo

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

1375ptsvs1315pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

12.2%vs9.2%
Terminal-Bench (Agentic Coding & Terminal Use)

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

41.5%vs30.5%
SciCode (Scientific Computing)

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

40%vs32%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

67%vs56.5%
AIME (Advanced Math Competition)

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

89.6%vs84%
Long-Context Reasoning

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

78%vs68.5%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

21 tok/s vs17 tok/s

Latence et Temps de Réponse

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
GPT Image 1.57.10s
Est. End-to-End Response (500 tokens)TTFT + generation time
GPT Image 230.01s
GPT Image 1.536.51s

Simulateur Interactif de Coût de Tokens

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
GPT Image 1.5 Monthly API Bill
$43.50 / month
$5/1M in · $40/1M out
These models cost the same under this workload — no savings either way.Break-even

GPT Image 2 · Strengths & Key Advantages

  • le plus élevé image qualité dans le GPT image line
  • Excellent compositional et lighting control
  • fiable multitour image editing
  • Excellent à accurate dans-image texte et typography
Considerations & Trade-offs
  • Premium tarification par generated image
  • génération latence still élevé pour interactif design

GPT Image 1.5 · Strengths & Key Advantages

  • meilleur prompt fidelity que GPT image 1
  • Cleaner typography et layout control
  • Improved consistency à travers multi-image edits
  • meilleur à préservant subject identity à travers edits
Considerations & Trade-offs
  • Still expensive pour bulk génération
  • Occasional sur-smoothing sur detailed textures

Token Use & Task Economics

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

MetricGPT Image 2GPT Image 1.5
Cost per Task
Estimated USD to finish one task
$0.36$0.34
Output Tokens / Task
Tokens generated per task
9K9K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Spécifications Techniques

Architecture, release timeline, capabilities, and modalities.

CaractéristiqueGPT Image 2GPT Image 1.5
Official API Model ID (SDK)gpt-image-2gpt-image-1-5
API ProtocolOpenAI Responses API & Chat Completions v1OpenAI Responses API & Chat Completions v1
Release Date2026-042025-11
Knowledge CutoffMarch 2026October 2025
ArchitectureFrontier native multimodal image 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 ($5/1M in) across full 4K windowFlat rate ($5/1M in) across full 4K window
Prompt Cache Policy & TTLAutomatic prefix caching at $0/1M (no extra cache-write fee)Automatic 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 CallingPrompt-based JSON extraction onlyPrompt-based JSON extraction only
Web Search Grounding & SandboxNative Web Search tool + Hosted Code Interpreter & File Search RAGNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)Supervised 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 ClassificationOpenAI Preparedness Framework Audited & System Card PublishedOpenAI 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

GPT Image 2 vs GPT Image 1.5: Réponses Rapides (FAQ)

Which is more intelligent, GPT Image 2 or GPT Image 1.5?

GPT Image 2 is more intelligent. It scores 59 compared with 46 on the composite Intelligence Index.

Which model is faster?

GPT Image 2 is faster, generating 21 tokens per second compared with 17 tokens per second.

Which model has lower latency?

GPT Image 2 has lower latency, with a time to first token of 6.20s compared with 7.10s.

Verdict

Verdict & Model Selection Recommendation: Choose GPT Image 2 if your priority is High-end visual production and campaign assets. 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.

Questions fréquentes

How does GPT Image 2 compare to GPT Image 1.5 in API token pricing and cost per token?+

Both GPT Image 2 and GPT Image 1.5 share matched API token pricing at $5.00 per 1M input tokens and $40.00 per 1M output tokens. Because their token economics are identical, your selection should be guided by benchmark latency, memory horizon, and reasoning capabilities rather than operational token fees.

Which model has a larger context window: GPT Image 2 or GPT Image 1.5?+

Both GPT Image 2 and GPT Image 1.5 offer an identical context window of 4K tokens (~3k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.

Which AI model is better for software engineering and coding: GPT Image 2 or GPT Image 1.5?+

GPT Image 2 scores 68.5% on SWE-bench Verified while GPT Image 1.5 scores 52%. For autonomous software engineering and multi-file debugging, GPT Image 2 holds the verified benchmark lead.

What is the difference in reasoning capability and extended thinking between GPT Image 2 and GPT Image 1.5?+

GPT Image 2 is optimized for low-latency direct generation. In contrast, GPT Image 1.5 focuses on standard fast inference.

How does inference speed (tokens per second) compare between GPT Image 2 and GPT Image 1.5?+

GPT Image 2 generates approximately 21 tokens per second, while GPT Image 1.5 delivers 17 tokens per second. In latency-sensitive production environments, GPT Image 2 provides faster time-to-first-token (TTFT) and higher streaming response velocity.

Can you implement a tiered LLM routing setup combining GPT Image 2 and GPT Image 1.5 to optimize costs?+

Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to GPT Image 2, and route deep architectural or complex reasoning queries to GPT Image 2. This balanced routing strategy maximizes system throughput and answer quality.

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.

Sources: GetInfoUs Independent AI Benchmarks & Official API Specs