gpt-5-search-apiGPT-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.
Select an endpoint and copy a working example for this model.
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-5-search-api", 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-5-search-api",# messages=[{"role": "user", "content": "Hello!"}],# extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )modelmessagesmax_tokenstemperaturetop_pstreamtoolsreasoning_effortstream_optionsthinkingextra_bodyUse these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.
See how this model compares to others from the same provider.
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.
See how this model compares to others from the same provider.
GPT-5.6 Sol Pro uses the same underlying model as GPT-5.6 Sol, but runs with reasoning.mode set to pro for higher-quality responses on complex tasks. Optimized for deeper reasoning and more reliable execution, it is particularly well suited for advanced coding, long-horizon problem solving, and agentic workflows where accuracy and solution quality take priority over speed and cost.
GPT-5.4 Pro is OpenAI's most advanced model, built on the unified GPT-5.4 architecture with enhanced reasoning capabilities for complex and high-stakes tasks. It supports text and image inputs and features a 1M+ token context window (≈922K input, 128K output) for handling large-scale workflows and long-context analysis. Optimized for step-by-step reasoning, instruction following, and accuracy, GPT-5.4 Pro excels in agentic coding, long-context problem solving, and complex multi-step workflows, making it well suited for advanced engineering, research, and high-reliability applications.
GPT-5 Codex is a coding-focused version of GPT-5 built for both interactive development and long autonomous engineering tasks. It can create projects, add features, debug, refactor, and review code, producing cleaner and more controllable outputs than GPT-5. It integrates with developer tools (CLI, IDEs, GitHub, cloud), supports adjustable reasoning effort, handles multimodal inputs, and uses tools for search and environment setup — making it purpose-built for agentic coding workflows.
Initialized observational baseline with no recorded failures