Prompt Engineering: OpenAI Reasoning Models o1, o3 and o4

Explore OpenAI reasoning models, including o3 and o4 families, their mini variants, and how availability and API access affect accuracy, speed, and cost. Learn to balance reasoning effort for tasks.

Learn how to create an OpenAI account, navigate the OpenAI platform and ChatGPT, set up billing with $5 minimum, and access the playground for API calls in a developer workflow.

Get a high level overview of o1, o3, and o4 models, with o3 as the current best full model and o4 mini offering faster, cheaper reasoning.

Trace OpenAI’s first reasoning model o1, codenamed strawberry, its reinforcement learning enhanced chain-of-thought, and why it’s retired, plus how to compare remaining models like o1 pro, mini, and GPT-4.

Verify your OpenAI account by completing the organization verification with persona, submitting a government-issued ID and a selfie, which instantly confirms and unlocks models like O3.

Learn how the O3 reasoning model uses reinforcement learning and tools in its chain-of-thought, including web search, file analysis, and image generation, comparing O1, O3 mini, and O4 mini.

OpenAI o4 mini replaces o3 mini as the fastest, cost-efficient reasoning model, excelling in math, coding, and visual reasoning, with benchmarks showing it outperforming o3 and becoming the best choice.

Compare standard non-reasoning models such as GPT-4 with reasoning models in a math task to illustrate speed versus accuracy, noting fast but sometimes incorrect results versus slower, self-checking corrections.

Explore how visual reasoning models from OpenAI integrate images with text to solve problems using both visual and written cues, extracting structured data from images like charts and diagrams.

Compare GPT models’ speed and cost with O-series reasoning models’ accuracy for complex, multi-step problem solving, and learn when to deploy architecture planning versus execution in OpenAI workflows.

Compare standard GPT and reasoning models side by side using the playground's compare tool, assess speed, cost, and accuracy across tasks to decide the best model for your use case.

Learn to craft clear, structured prompts for reasoning models, using delimiters and formats like JSON or markdown to supply data and context for faster results.

Set voice, role, and tone to tailor AI responses in OpenAI reasoning models o1, o3, and o4 for concise communication that targets executives and ROI.

Explore ask-first prompting, a clarification-before-generation technique that uses follow-up questions to gather context, improve collaboration with language models, and deliver more accurate, aligned responses for complex tasks.

Discover zero-shot prompting, where you assign a task to a model without examples and use baseline performance to guide prompt engineering for reasoning models.

Explore few-shot prompting to guide model responses through examples, shaping format and style instead of explicit instructions, and compare zero-shot prompting to control output length and content.

Explore chain-of-thought prompting, showing how adding explicit stepwise reasoning affects model outputs, compares reasoning models to standard prompts, and clarifies when chain-of-thought helps or harms performance.

Explore meta prompting to have a reasoning model craft and refine prompts, evaluate improvements, and iterate with AI collaboration for clearer, more effective prompts.

Learn how to avoid prompting mistakes for reasoning models, such as unnecessary chain of thought prompts, irrelevant context, overusing few-shot examples, and unclear output structure, to improve accuracy and efficiency.