Improved prompt-driven génération, editing et visuel design flux de travail.
Technical Specifications
- Provider
- OpenAI
- Category
- flagship
- Release Date
- 2025-11
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- $20 / month
- Reasoning Effort Control
- Fixed
- Structured Outputs
- No
- Output Modalities
- image
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- gpt-image-1-5
- Supported SDK & API Protocol
- OpenAI Responses API & Chat Completions v1
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2027+)
- Production Rate Limits (RPM & TPM)
- 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
- Long-Context Pricing Tier (>128K / >200K)
- Flat rate ($5/1M in) across full 4K window
- Prompt Cache Write & TTL Policy
- Automatic prefix caching at $0/1M (no extra cache-write fee)
- Computer Use & Browser GUI Agent
- Supported (Screen coordinate grounding, browser & desktop GUI action loops)
- MCP & Parallel Tool Calling
- Prompt-based JSON extraction only
- Web Search Grounding & Code Sandbox
- Native Web Search tool + Hosted Code Interpreter & File Search RAG
- Fine-Tuning & Distillation Support
- Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
- Weights Precision & Quantization
- Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster)
- Minimum GPU VRAM Requirement
- Zero Local VRAM Required (100% Serverless Cloud API)
- Supported Inference Engines
- Managed OpenAI Cloud API
- System Card & Safety Framework
- OpenAI Preparedness Framework Audited & System Card Published
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
GPT Image 1.5 Cost, Context, Hardware & API Integration
For single-user chat, a $20/mo subscription caps your bill below this API volume.
Break-even point: ~13 requests/day equals $20/month.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to GPT Image 1.5:
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 52%
- GPQA Diamond
- 66%
- MMLU-Pro
- 78.5%
- HumanEval
- 91%
- Humanity's Last Exam
- 9.2%
- Terminal-Bench
- 30.5%
- SciCode
- 32%
- LiveCodeBench
- 56.5%
- AIME
- 84%
- Long-Context Reasoning
- 68.5%
- Chatbot Arena Elo
- 1315pts
Strengths
- 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
Limitations & Trade-offs
- Still expensive pour bulk génération
- Occasional sur-smoothing sur detailed textures
Editorial Assessment
un solid refinement rather que un reinvention de GPT image 1.
Best for: Design et marketing flux de travail needing fiable prompt adherence
Compare GPT Image 1.5
Frequently Asked Questions about GPT Image 1.5
How much does the GPT Image 1.5 API cost per 1M tokens?+
GPT Image 1.5 by OpenAI is priced at $5 per 1M input tokens and $40 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the GPT Image 1.5 pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in GPT Image 1.5's context window?+
GPT Image 1.5 features a 4K tokens context window, which equals approximately 3,072 English words or ~8 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 4,096 tokens.
Can I run GPT Image 1.5 locally on my own GPU (Open Weights)?+
No, GPT Image 1.5 is a proprietary closed-weights model hosted by OpenAI. It cannot be downloaded for local GPU execution and must be accessed via OpenAI's official API or cloud platform.
Does OpenAI train on data sent to the GPT Image 1.5 API, and is commercial use allowed?+
API payloads are excluded from model training by default. License tier: Proprietary Commercial API (OpenAI Terms). Full commercial ownership of generated outputs. Supported deployment channels include: OpenAI Platform API, Microsoft Azure OpenAI Foundry.
How does GPT Image 1.5 perform on coding and reasoning benchmarks?+
GPT Image 1.5 achieves a composite Intelligence Index of 46/100, scoring 52% on SWE-bench Verified, 66% on GPQA Diamond, 78.5% on MMLU-Pro, and 1315 pts on Chatbot Arena Elo.
What is GPT Image 1.5 best used for?+
GPT Image 1.5 is best suited for: Design et marketing flux de travail needing fiable prompt adherence. Assessment: un solid refinement rather que un reinvention de GPT image 1.
OpenAI Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to OpenAI's flagship reasoning tier:
GPT-6 Astra
GPT-6.1 Sol
GPT-6 Sol
GPT-6 Luna
GPT-5.6 Sol
GPT-5.6 Terra
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to GPT Image 1.5:
L'unité textuelle de base traitée par un modèle linguistique, représentant généralement 3 à 4 caractères ou environ 0,75 mot. Par exemple, une phrase courante est découpée en plusieurs jetons.
Une suite de tests standardisés permettant d'évaluer et de comparer objectivement les performances des modèles d'IA sur des tâches ciblées (code avec SWE-bench, mathématiques avec AIME, etc.).
Le nombre maximal de jetons qu'un modèle peut traiter simultanément (entrée et sortie comprises). Une fenêtre de 1 000 000 jetons permet d'analyser environ 750 000 mots en une seule requête.
Un réseau neuronal multimodal conçu pour analyser, raisonner et générer conjointement des données visuelles (images, schémas, interfaces) et du langage naturel au sein d'une même structure.
L'architecture de réseau neuronal introduite en 2017 à la base des LLM modernes, utilisant l'auto-attention pour traiter tous les jetons en parallèle plutôt que séquentiellement.
Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: OpenAI Official Pricing & Technical Reports (Late 2026)↗