- Image Generation
Recraft V4 is Recraft’s third-generation image generation model, built around professional design workflows and visual taste, with a focus on photorealism, refined composition, and high-quality raster…
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GPT-5 Image
- Image Generation
GPT-5 Image is an OpenAI multimodal model variant focused on understanding and generating images, extending GPT-5’s capabilities beyond text to visual content.
Providers Comparison
Speed
Output tokens per second · Higher is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
End-to-End Response Time
Seconds · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
Price
USD per 1M tokens (blended) · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
About the model
What is GPT-5 Image?
GPT-5 Image is an OpenAI model designed to interpret and create images as part of a broader multimodal AI system. It is mainly used for tasks such as image understanding, description, and visual question answering. It can also support workflows that combine text and imagery, like design ideation, document analysis, and educational content creation. It follows earlier OpenAI vision-capable models such as GPT-4 with vision and other GPT-5 family variants.
Model capabilities
5 Core Capabilities
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Image Understanding
Analyzes uploaded images, identifying objects, people, scenes, and visual relationships, and can answer detailed questions about visual content.
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Optical Character Recognition
Reads and extracts printed or handwritten text from images, screenshots, documents, and photos, preserving structure where possible.
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Multimodal Chat
Engages in interactive conversations mixing text and images, allowing users to reference visuals directly within natural language dialogue.
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Content Moderation
Assists in monitoring visual and textual content for policy-violating material, supporting safer, compliant applications and user experiences.
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Image-Based Translation
Translates text found within images or screenshots into other languages while retaining awareness of surrounding visual context and layout.
Use cases
6 Most Valuable Use Cases
- Visual Code Debugging
- Contract Clause Review
- Legal Case Monitoring
- Retail Shelf Analysis
- Invoice Line Extraction
- Image Content Tagging
Transparent pricing
Cost Comparison
LLM API offers the lowest prices and best limits for GPT-5 Image–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~180ms | ~120 img/min | 99.99% | ~$0.0004/img | ~$0.0004/img | ~64K tokens + 8 images |
| OpenAI | Global | ~220ms | ~80 img/min | 99.9% | ~$0.0010/img | ~$0.0010/img | ~32K tokens + 4 images |
| Azure OpenAI | US East | ~240ms | ~70 img/min | 99.9% | ~$0.0011/img | ~$0.0011/img | ~32K tokens + 4 images |
| Google Cloud (Gemini Vision-equivalent) | Global | ~260ms | ~60 img/min | 99.9% | ~$0.0012/img | ~$0.0012/img | ~32K tokens + 4 images |
| Anthropic (Claude Vision-equivalent) | US West | ~250ms | ~55 img/min | 99.9% | ~$0.0013/img | ~$0.0013/img | ~32K tokens + 4 images |
Performance benchmarks
Technical Specifications
| Metric | GPT-5 Image (OpenAI) | Gemini Vision Pro (Google) | Claude 3.7 Vision (Anthropic) |
|---|---|---|---|
| Latency per Image | ~800ms | ~900ms | ~1.0s |
| Throughput | ~40 img/s | ~35 img/s | ~30 img/s |
| Max Resolution | ~4096x4096 | ~4096x4096 | ~4096x4096 |
| Price per Image | ~$0.005 | ~$0.006 | ~$0.007 |
| Supported Formats | PNG, JPG, WEBP, GIF~ | PNG, JPG, WEBP~ | PNG, JPG, WEBP~ |
| Uptime | ~99.9% | ~99.9% | ~99.9% |
30-day usage via LLM API
- 3.8B
- Image prompts processed (30 days)
- 95M
- API requests served (30 days)
- 12.4M
- Unique developer & app integrations (30 days)
- 99.96%
- Avg uptime over last 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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Unified AI Routing
Dynamically route each request to the optimal model across providers based on latency, cost, and quality—without changing your integration or redeploying code.
One endpoint, every model. -
Cost-Aware Orchestration
Enforce budgets, choose cheaper equivalents, and mix premium and commodity models automatically so you control spend without manually tuning every call.
Max performance, minimal spend. -
Resilient Fallback Logic
Survive provider outages and rate limits with automatic cross-vendor failover and graceful degradation policies, keeping your AI features online by default.
Stay up when others fail. -
End-to-End Observability
Trace every request across models and providers with unified logs, metrics, and latency breakdowns so you can debug, optimize, and prove reliability in production.
See every token, everywhere. -
Task-Level Abstractions
Define high-level tasks—chat, extraction, scoring, tools—and let LLM.API pick and configure the right models so you ship features, not prompt glue.
Code tasks, not prompts. -
High-Throughput Batch Jobs
Run massive offline jobs across providers with parallelized batching, retry policies, and progress tracking so you can process millions of items reliably and cheaply.
Scale from 10 to millions.
Decision guide
When to Use — When NOT to Use
Use it if...
- You need high-quality image understanding, including OCR, charts, and natural photographs.
- You need to extract structured data or descriptions from complex, mixed-content screenshots.
- Your use case involves multimodal agents that must interpret UI layouts and visual states.
- Your use case involves analyzing product images for attributes, defects, or categorization.
- You need to combine image interpretation with strong language reasoning in a single model.
- Your use case involves visual question answering over documents, diagrams, and web pages.
- You need to automate manual visual review tasks, like invoice checks or ID verification.
Avoid if...
- You need ultra-low-cost image processing at massive scale with minimal per-image reasoning.
- Your workload requires hard real-time latency guarantees on-device without network connectivity.
- You need specialized 3D vision, pose estimation, or medical imaging models with certifications.
- Your workload requires strict on-premise deployment where cloud-hosted APIs are disallowed.
- You need deterministic, bit-for-bit reproducible outputs for safety-critical computer vision pipelines.
- Your workload requires generating images rather than analyzing or understanding existing images.
- You need domain-specific fine-tuned vision models that you can train and host yourself.
FAQ
Frequently Asked Questions
-
What is GPT-5 Image?
GPT-5 Image is an OpenAI multimodal model for image understanding and generation, accessible via the unified LLM.API gateway.
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What is GPT-5 Image best suited for?
GPT-5 Image is best for tasks combining images and text, such as visual question answering, image description, UI understanding, and image editing instructions.
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How is GPT-5 Image priced when used through LLM.API?
GPT-5 Image pricing is defined by LLM.API’s OpenAI GPT-5 Image tariff; check your LLM.API dashboard or pricing docs for up-to-date per-token and image rates.
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What context window does GPT-5 Image support via LLM.API?
GPT-5 Image supports a large-context text window determined by the underlying OpenAI deployment; see the LLM.API model card for the current token limit.
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How fast is GPT-5 Image in terms of latency?
GPT-5 Image latency depends on input size and load, but LLM.API maintains persistent connections and routing to minimize typical response times.
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Which input and output modalities does GPT-5 Image support?
GPT-5 Image supports text-plus-image inputs and text outputs, with image-related reasoning and editing instructions handled in a single multimodal endpoint.
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How do I call GPT-5 Image through the LLM.API platform?
Use the LLM.API completion or chat endpoint with the provider set to OpenAI and the model set to gpt-5-image, passing image URLs or bytes as inputs.
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How does GPT-5 Image compare to other OpenAI vision models on LLM.API?
Compared to earlier OpenAI vision models, GPT-5 Image generally offers better reasoning, more accurate descriptions, and stronger instruction-following on complex multimodal tasks.
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Does GPT-5 Image have any important limitations?
GPT-5 Image can misinterpret ambiguous images, hallucinate details, and should not be solely relied on for safety-critical or privacy-sensitive visual analysis.
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Can GPT-5 Image handle large batches or streaming responses via LLM.API?
GPT-5 Image supports batching and streaming where enabled by LLM.API, allowing higher throughput and incremental token delivery for long multimodal responses.
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