GPT flagship and reasoning
Complex reasoning, coding, and agents
Start here for difficult planning, multi-step engineering, tool use, and work where output quality matters more than minimum latency.
Browse matching modelsFull-capability model access · OpenAI GPT models
GPT full-capability API across flagship, compact, reasoning, and image models
Use this page to compare GPT model families, understand what full-capability access means, and open the current catalog for exact model IDs and pricing.
Provider family
OpenAI GPT
Common interfaces
Chat, Responses, Images
Selection rule
Match model tier to workload
Current GPT model families
The catalog is the source of truth for exact model IDs. These groups explain the main workload split without freezing the page to one release number.
GPT Mini and Nano
Fast and high-volume requests
Use compact tiers for extraction, classification, summaries, routine coding assistance, and workloads that need tighter cost or latency control.
Browse matching modelsGPT Image
Image generation and editing
Use image-specific models for text-to-image, reference-image work, visual assets, and workflows that return image URLs or image task results.
Browse matching modelsUse-case fit
Where GPT model families fit
Tools and planning01
Coding and agent workflows
GPT models are commonly evaluated for code generation, repository changes, tool calls, structured outputs, and long task chains.
Quality and throughput02
Production text workloads
Different GPT tiers let teams trade reasoning depth for latency and cost across support, extraction, content, and internal automation.
Vision and generation03
Multimodal and image work
Select a model that explicitly supports the required input and output mode instead of assuming every GPT model handles the same media features.
User decision signals
What to verify before choosing a GPT model
Model names, endpoints, supported tools, pricing, and availability can change. Use the current catalog and run a controlled test before production traffic.
Provider family
OpenAI GPT
Common interfaces
Chat, Responses, Images
Selection rule
Match model tier to workload
Selection logic
What to verify before choosing a GPT model
01
Full-capability is not a single model name
It means selecting the intended model tier and using an endpoint that preserves the capabilities exposed for that exact model.
02
Chat, Responses, and Images are different workflows
Check the supported endpoint type before integration. A model available for chat is not automatically available for Responses or image generation.
03
The live catalog is the final reference
Use the current model directory for exact IDs, pricing plans, supported inputs, tools, and availability before sending larger traffic.
Real user questions
GPT full-capability API FAQ
Q01
What does GPT full-capability API mean here?
It means access to the listed GPT model tier through a compatible endpoint without treating all GPT names as interchangeable. Exact capabilities follow the current model catalog.
Q02
Should I always choose the largest GPT model?
No. Use a flagship or reasoning tier for difficult work, and compare compact tiers for routine or high-volume tasks where latency and cost matter more.
Q03
How do I find the exact GPT model ID?
Open the model catalog from this page, review the supported endpoint and pricing, then copy the complete model ID shown there.
OpenAI GPT models
Test the exact GPT model ID with a real workload
Model names, endpoints, supported tools, pricing, and availability can change. Use the current catalog and run a controlled test before production traffic.