Head-to-Head Model Comparison

GPT Image 2 vs Claude Opus 5.5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Vérifié par l’équipe de recherche de GetInfoUsDernière révision : October 2026Politique éditoriale
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Compare GPT Image 2 by OpenAI against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.

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%

Anthropic

Claude Opus 5.5

Titan d'intelligence de pointe

d'Anthropic pinnacle modèle de pointe conçu pour à long terme agentic ingénierie logicielle, dense multimodal document synthèse, et autonome recherche flux de travail.

Intelligence Index94 / 100
Input Token:

$4.00 / 1M

Output Token:

$20.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

84.6%

Différences Clés en un Coup d’Œil

  • Claude Opus 5.5 is 1.8x cheaper for API usage (input + output tokens combined).
  • Claude Opus 5.5 has a larger context window (1M vs 4K tokens).
  • Claude Opus 5.5 is faster at 78 tok/s vs 21 tok/s.
  • Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 68.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
CaractéristiqueGPT Image 2Claude Opus 5.5
Intelligence Index
Composite reasoning & knowledge score (0-100)
5994
Provider
OpenAIAnthropic
Input Price
Cost per million input tokens
$5.00 / 1M$4.00 / 1M
Output Price
Cost per million output tokens
$40.00 / 1M$20.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$5.00 / 1M$3.08 / 1M
Context Window
Maximum input + output tokens
4.1K tokens~6 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
4K128K
Throughput Speed
Tokens generated per second
21 tok/s78 tok/s
SWE-bench Verified
Real-world software engineering
68.5%84.6%
GPQA Diamond
Graduate-level reasoning
72.5%82.3%
Cached Input Price
Prompt caching discount per 1M tokens
$0.00 / 1M$0.40 / 1M

Qui gagne où ?

At-a-glance decision framework
Code
Claude Opus 5.5
+16.1% SWE-bench lead
Coût
Claude Opus 5.5
1.8x cheaper monthly runtime
Vitesse
Claude Opus 5.5
+271% faster generation
Contexte
Claude Opus 5.5
1M tokens window

Scores de Benchmarks Vérifiés

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

GPT Image 2Claude Opus 5.5
SWE-bench Verified (Autonomous Coding)

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

68.5%vs84.6%
GPQA Diamond (Doctorate-Level Science & Math)

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

72.5%vs82.3%
MMLU-Pro (Multi-task Language Understanding)

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

84.8%vs91.5%
LMSYS Chatbot Arena Elo

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

1375ptsvs1507pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

12.2%vs19.8%
Terminal-Bench (Agentic Coding & Terminal Use)

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

41.5%vs66.4%
SciCode (Scientific Computing)

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

40%vs58.7%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

67%vs83.1%
AIME (Advanced Math Competition)

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

89.6%vs95.4%
Long-Context Reasoning

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

78%vs92.6%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

21 tok/s vs78 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
Claude Opus 5.51.40s
Est. End-to-End Response (500 tokens)TTFT + generation time
GPT Image 230.01s
Claude Opus 5.57.81s

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
Claude Opus 5.5 Monthly API Bill
$24.60 / month
$4/1M in · $20/1M out
Claude Opus 5.5 saves approximately $18.90 / month (43%) under this workload.1.8x Savings

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

Claude Opus 5.5 · Strengths & Key Advantages

  • inégalé autonome multi-étapes codage agentique et refactorisation
  • Flawless multidocument raisonnement sur 1M token contexte avec no long-prompt surcharge
  • le plus élevé Artificial analyse intelligence Index ever measured (58/100), leading SWE-bench Pro à 89.9%
  • Precise outil exécution avec robust error-recovery boucles
Considerations & Trade-offs
  • Premium sortie tarification ($20.00/M) plus heavy raisonnement-token usage (~119K sortie tokens par index tâche) keeps long agentic fonctionne expensive
  • long pensée times à maximum effort peut push un unique hard agentic tâche toward ~$13

Token Use & Task Economics

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

MetricGPT Image 2Claude Opus 5.5
Cost per Task
Estimated USD to finish one task
$0.36$3.85
Output Tokens / Task
Tokens generated per task
9K68K
Reasoning Tokens / Task
Thinking tokens burned per task
0K42K

Spécifications Techniques

Architecture, release timeline, capabilities, and modalities.

CaractéristiqueGPT Image 2Claude Opus 5.5
Official API Model ID (SDK)gpt-image-2claude-opus-5-5-20260901
API ProtocolOpenAI Responses API & Chat Completions v1Anthropic Messages API + AWS Bedrock + Vertex AI
Release Date2026-042026-09
Knowledge CutoffMarch 2026June 2026
ArchitectureFrontier native multimodal image transformerExtended Autonomous Hybrid Reasoning Transformer
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($5/1M in) across full 4K windowStandard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLAutomatic prefix caching at $0/1M (no extra cache-write fee)$0.4/1M cached read (90% off); 5-min default & 1-hour extended 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 RAGNative Web Search Citations + Code Execution Tool + MCP Connectors
Official Fine-Tuning SupportSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)Custom Model Tuning via Amazon Bedrock & Prompt 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 PublishedAnthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated
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 Modalitiesimagetext

GPT Image 2 vs Claude Opus 5.5: Réponses Rapides (FAQ)

Which is more intelligent, GPT Image 2 or Claude Opus 5.5?

Claude Opus 5.5 is more intelligent. It scores 94 compared with 59 on the composite Intelligence Index.

Which model is faster?

Claude Opus 5.5 is faster, generating 78 tokens per second compared with 21 tokens per second.

Which model is cheaper to run?

Claude Opus 5.5 is cheaper at $3.08 per 1M tokens blended, compared with $5.00 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Claude Opus 5.5 has lower latency, with a time to first token of 1.40s compared with 6.20s.

Which model has a larger context window?

Claude Opus 5.5 has a larger context window, supporting 1M tokens compared with 4.1K tokens.

Verdict

Choose GPT Image 2 for élevé-end visuel production et campaign assets, or choose Claude Opus 5.5 for grand-échelle logiciel development agents, d'entreprise codebases, et mission critique scientifique synthèse.

Questions fréquentes

Which is cheaper: GPT Image 2 or Claude Opus 5.5?+

GPT Image 2 costs $5/1M input and $40/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.

Which model has a larger context window: GPT Image 2 or Claude Opus 5.5?+

GPT Image 2 supports 4K tokens whereas Claude Opus 5.5 supports 1M tokens.

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: OpenAI & Anthropic Official Documentation