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GPT-5.4 Image 2
- Text Generation
GPT-5.4 Image 2 is an OpenAI multimodal model that can understand and generate both text and images. It is notable for combining advanced language capabilities with high-quality image understanding and creation.
About the model
What is GPT-5.4 Image 2?
GPT-5.4 Image 2 is a multimodal OpenAI model designed to process and generate text and images. It is mainly used for tasks such as describing, analyzing, or transforming images using natural language, and for creating or editing images from textual instructions. It is also applied in interactive applications that need both conversational intelligence and visual understanding, such as assistants, design tools, and educational platforms. It follows earlier OpenAI GPT and image models, extending that family with tighter integration of vision and language.
Model capabilities
5 Core Capabilities
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Multimodal Chat
Engages in interactive conversations, following complex instructions and maintaining context across long dialogues for varied assistant-style tasks.
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Image Interpretation
Accepts images as input and explains visual content, answering questions about objects, layouts, charts, and other scene details.
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Visual Text Reading
Reads and transcribes text embedded in images, such as documents, signs, screenshots, and handwritten notes, supporting downstream reasoning tasks.
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Code and Tools
Helps write and analyze code, and can integrate with tools or APIs when available to solve more complex workflows.
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Multilingual Handling
Understands and generates multiple languages, enabling cross-lingual question answering, drafting, and basic translation-style assistance tasks.
Use cases
6 Most Valuable Use Cases
- Product Photo Generation
- Marketing Visual Design
- UI Mockup Creation
- Chart And Diagram Rendering
- Legal Diagram Illustration
- Vision Model Prototyping
Transparent pricing
Cost Comparison
LLM API offers the lowest per-image costs and best SLAs for GPT‑5.4 Image 2–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~180ms | ~120 img/min | 99.99% | ~$0.050/img | ~$0.050/img | ~32K tokens + 10 images |
| OpenAI | Global | ~250ms | ~80 img/min | 99.9% | ~$0.080/img | ~$0.080/img | ~32K tokens + 10 images |
| Azure OpenAI | US East | ~280ms | ~70 img/min | 99.9% | ~$0.090/img | ~$0.090/img | ~32K tokens + 10 images |
| Anthropic (Claude Vision-equivalent) | US West | ~320ms | ~480 img/min | 99.9% | ~$0.0014/img | ~$0.0014/img | ~32K tokens + 8 images |
| Google (Gemini Vision-equivalent) | Global | ~340ms | ~450 img/min | 99.9% | ~$0.0015/img | ~$0.0015/img | ~32K tokens + 8 images |
Performance benchmarks
Technical Specifications
| Metric | GPT-5.4 Image 2 | Gemini 2.0 Flash Image | Claude 3.7 Sonnet Vision |
|---|---|---|---|
| Latency per Image | ~180ms | ~220ms | ~250ms |
| Throughput | ~40 img/s | ~30 img/s | ~25 img/s |
| Max Resolution | 4096×4096 | 4096×4096 | 3072×3072 |
| Price per Image | $0.002 | $0.0025 | $0.003 |
| Supported Formats | JPEG, PNG, WEBP | JPEG, PNG, WEBP | JPEG, PNG |
| Uptime | 99.9% | 99.9% | 99.5% |
30-day usage via LLM API
- 3.8B
- Prompt tokens processed (last 30 days)
- 420M
- Image generation tokens (30 days)
- 64M
- API requests served (30 days)
- 99.9%
- Avg API uptime (30 days)
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Intelligent Model Routing
Automatically route each request to the optimal model across providers based on latency, cost, and quality—no client changes, just smarter responses over time.
One endpoint, every model -
Cost-Aware Execution
Enforce per-project and per-request cost controls with transparent pricing across providers so you can experiment freely, ship faster, and avoid billing surprises at scale.
Control spend by design -
Resilient Fallback Flows
Define automatic failover to backup models and providers on errors, timeouts, or rate limits so your production workloads stay online without custom retry logic.
No single point of failure -
Deep LLM Observability
Get centralized logs, traces, and metrics for every provider and model—spot regressions, debug prompts, and tune routing rules from one observability layer.
See every token, everywhere -
Task-Level Orchestration
Describe tasks, not providers—LLM.API picks tools, models, and parameters, letting you iterate on behavior instead of wiring low-level AI plumbing.
Ship tasks, not glue code -
High-Throughput Batch Jobs
Run large-scale batch inference with concurrency, retries, and progress tracking handled for you—perfect for backfills, reprocessing, and offline evaluation pipelines.
Batch at production scale
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a single model that can understand both images and text together.
- You need high-quality image understanding for UI screenshots, charts, and dense diagrams.
- Your use case involves multimodal agents that reason over photos, documents, and web pages.
- You need reliable extraction of structured data from complex images, dashboards, or forms.
- Your use case involves visually grounded reasoning, like comparing product photos or layouts.
- You need to explain, summarize, or caption images in natural, fluent English text.
- Your use case involves multi-turn troubleshooting using both photos and textual logs together.
Avoid if...
- You need a minimal, cheapest-possible text-only model without any image capabilities.
- Your workload requires strict offline deployment with no dependence on external APIs.
- You need deterministic, bit-for-bit reproducible outputs for regulatory or safety certification.
- Your workload requires hard real-time guarantees or ultra-low latency edge inference.
- You need to process only simple, short text queries where smaller models suffice.
- Your workload requires training or fine-tuning the base vision model directly on-premise.
- You need a fully open-source vision-language stack that can run entirely on your hardware.
FAQ
Frequently Asked Questions
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What is GPT-5.4 Image 2?
GPT-5.4 Image 2 is an OpenAI multimodal model accessible via LLM.API, designed for combined image understanding and high-quality text generation.
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What is GPT-5.4 Image 2 best suited for?
It excels at image captioning, visual question answering, UI or chart understanding, and generating detailed text grounded in complex visual inputs.
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What modalities does GPT-5.4 Image 2 support?
GPT-5.4 Image 2 accepts image and text inputs and returns text outputs through the unified LLM.API interface.
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How is GPT-5.4 Image 2 priced on LLM.API?
LLM.API handles metering and billing, so you pay per token and image usage according to LLM.API’s OpenAI GPT-5.4 Image 2 pricing tier.
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What is the context window of GPT-5.4 Image 2?
GPT-5.4 Image 2 supports a large-token text context window suitable for multi-step reasoning over long prompts and image-derived descriptions.
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How fast is GPT-5.4 Image 2 in terms of latency?
Typical latency is higher than lightweight text-only models but remains suitable for interactive applications, especially when using streaming responses.
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How do I call GPT-5.4 Image 2 through LLM.API?
Specify the provider as OpenAI and the model name as "gpt-5.4-image-2" in your LLM.API request, attaching images as supported media inputs.
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How does GPT-5.4 Image 2 compare to similar OpenAI models?
Compared to text-only GPT-5.x models, it adds advanced image understanding while keeping similar instruction-following, reasoning, and code-generation capabilities.
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Does GPT-5.4 Image 2 support streaming responses via LLM.API?
Yes, GPT-5.4 Image 2 can stream tokens through LLM.API, enabling partial responses to appear while the model is still generating.
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What limitations should I be aware of with GPT-5.4 Image 2?
It can still hallucinate facts, misinterpret ambiguous images, and should not be solely relied on for safety-critical or legally binding decisions.