OpenAI 推出的 GPT Image 1.5 是一款专注于高分辨率视觉创作、精准文字渲染与图像生成的大模型，具备 4K 上下文窗口、17 Token/秒生成速度与 46/100 综合智能指数（每百万 Token $5 输入 / $40 输出）。
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
- 提供 4K 上下文窗口与最高 4K 输出容量，核心基准表现优异（SWE-bench Verified 52% · GPQA Diamond 66%）
- 高达 17 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 7100 毫秒
- 极具竞争力的 API 定价（每百万 Token $5 输入 / $40 输出），原生支持提示词缓存、函数调用与结构化 JSON 输出
Limitations & Trade-offs
- 闭源专有云端模型，不支持下载权重进行离线本地物理隔离部署
- 在大规模高频调用场景下，建议结合提示词缓存 (Prompt Caching) 与批量 API 以控制成本
Editorial Assessment
GPT Image 1.5 凭借 46/100 的智能得分、4K 上下文容量与 17 Token/秒的响应速度，在 OpenAI 模型矩阵中提供了极佳的性能与成本平衡。
Best for: 专注于高分辨率视觉创作、精准文字渲染与图像生成的任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。
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: 专注于高分辨率视觉创作、精准文字渲染与图像生成的任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。. Assessment: GPT Image 1.5 凭借 46/100 的智能得分、4K 上下文容量与 17 Token/秒的响应速度，在 OpenAI 模型矩阵中提供了极佳的性能与成本平衡。
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:
The basic unit of text that a language model processes, typically representing about 3 to 4 English characters or roughly 0.75 words. For example, the sentence "Artificial intelligence is fast" breaks down into about 5 tokens.
A standardized evaluation suite used to objectively measure and rank an AI model's capabilities across specific tasks — such as SWE-bench Verified for software engineering, AIME for competitive math, or MMLU for general knowledge.
The maximum number of tokens a model can consider at once — both the input it reads and the output it writes. A 1,000,000-token context window can hold roughly 750,000 English words in a single request.
A multimodal neural network engineered to jointly process, reason over, and synthesize both visual content (images, diagrams, UI screenshots, video) and natural language text within a single framework.
The neural network architecture introduced in 2017 that powers modern LLMs, using self-attention mechanisms to process all tokens in a sequence concurrently rather than sequentially.
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.
数据来源: OpenAI Official Pricing & Technical Reports (Late 2026)↗