gpt-image-1gpt-image-1 is OpenAI's image generation model designed to create, edit, and enhance images from natural language prompts. It supports tasks like producing detailed visuals, modifying existing images, generating variations, and upscaling — making it useful for design, illustration, marketing assets, and creative exploration.
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from openai import OpenAI client = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.apertis.ai/v1") response = client.chat.completions.create( model="gpt-image-1", messages=[ {"role": "user", "content": "Hello!"} ], max_tokens=1024, temperature=0.7) print(response.choices[0].message.content) # Optional: Enable context compression to reduce token usage# response = client.chat.completions.create(# model="gpt-image-1",# messages=[{"role": "user", "content": "Hello!"}],# extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )modelpromptsizequalityresponse_formatnstylebackgroundoutput_formatUse these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.
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GPT-5.6 Luna Pro uses the same underlying model as GPT-5.6 Luna, but runs with reasoning.mode set to pro to deliver higher-quality responses on complex tasks. It is optimized for deeper reasoning, advanced coding, and multi-step agentic workflows, offering improved accuracy and solution quality while retaining the efficiency and scalability of the Luna tier.
GPT-5.6 Luna is the fast, cost-efficient model in OpenAI's GPT-5.6 series, optimized for high-volume, latency-sensitive workloads. It delivers capable reasoning at an affordable price point, making it ideal for chat applications, classification, and lightweight agentic workflows. Designed for scalable production deployments, GPT-5.6 Luna balances speed, cost, and reliability, providing efficient performance for real-time applications and large-scale automation tasks.
GPT-5.6 Terra Pro uses the same underlying model as GPT-5.6 Terra, but runs with reasoning.mode set to pro to deliver higher-quality responses on complex tasks. Optimized for deeper reasoning and greater reliability, it is well suited for advanced coding, multi-step reasoning, and agentic workflows where improved accuracy and solution quality are more important than maximizing speed or minimizing cost.
GPT-5.6 Terra is the balanced model in OpenAI's GPT-5.6 series, positioned between the flagship Sol tier and the cost-efficient Luna tier. It is designed for everyday coding, reasoning, and agentic workflows, delivering strong performance while balancing capability and cost. Offering near-flagship quality at approximately half the cost of Sol, GPT-5.6 Terra is well suited for production applications that require reliable reasoning, software development, and scalable agent execution.
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Codex Mini is a fine-tuned o4-mini model made for the Codex CLI. For most direct API usage, it’s better to use gpt-4.1 instead.
GPT-5.1 Codex is a coding-focused version of GPT-5.1 designed for both interactive development and long autonomous engineering tasks. It can build projects, add features, debug, refactor, and review code with higher steerability and cleaner outputs than GPT-5.1. It integrates with developer tools (CLI, IDEs, GitHub, cloud), supports adjustable reasoning effort, handles images/screenshots for UI work, and uses tools for search and environment setup — making it purpose-built for agentic coding workflows.
GPT-5 is OpenAI's most advanced model, built for complex, high-stakes tasks that require careful step-by-step reasoning and precise instruction following. It improves code quality, writing, and reliability, supports test-time routing and intent cues like “think hard,” and reduces hallucinations and sycophancy across demanding workloads.
GPT-4o Mini Transcribe is a smaller, cost-efficient speech-to-text model built on GPT-4o Mini's audio capabilities. It is designed for high-volume transcription workloads, delivering reliable performance with lower cost and latency. Priced per token (input and output), it provides transparent, fine-grained billing, making it well suited for scalable transcription pipelines, real-time applications, and cost-sensitive deployments.
Initialized observational baseline with no recorded failures