codex-miniCodex 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.
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from openai import OpenAI client = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.apertis.ai/v1") response = client.responses.create( model="codex-mini", input="Hello!", max_output_tokens=1024) print(response.output_text)modelinputinstructionsmax_output_tokensstreamtoolsreasoningtextstoreprevious_response_idmetadataUse 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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GPT-5.4 nano is the most lightweight and cost-efficient variant of the GPT-5.4 family, optimized for speed-critical and high-volume workloads. It supports text and image inputs and is designed for low-latency tasks such as classification, data extraction, ranking, and sub-agent execution. Prioritizing responsiveness and efficiency over deep reasoning, GPT-5.4 nano is ideal for real-time systems, background processing, and distributed agent pipelines where minimizing cost and latency is essential.
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-5.1 is the full-capability successor to GPT-5, offering stronger general reasoning, better instruction following, and a more natural conversational style. It uses adaptive reasoning to stay fast on simple questions while thinking more deeply on complex tasks, producing clearer, more grounded explanations. It shows steady improvements across math, coding, and structured analysis, with more coherent long-form output and more reliable tool use.
gpt-oss-20b is an open-weight, 21B-parameter OpenAI model released under Apache 2.0. It uses a Mixture-of-Experts design so only ~3.6B parameters run each step, enabling faster, lower-cost inference on consumer or single-GPU hardware. Trained in the Harmony format, it supports configurable reasoning depth, fine-tuning, function calling, tool use, and structured outputs.
Initialized observational baseline with no recorded failures