OpenAI’s O1 and O3-mini are advanced “reasoning” models that differ from the base GPT-4 (often referred to as GPT-4o) in how they process prompts and produce answers. These models are designed to spend more time “thinking” through complex problems, mimicking a human's analytical approach,
This section explores how O1 and O3-mini differ from GPT-4o in input handling, reasoning capabilities, and response behavior, and outlines prompt engineering best practices to maximize their performance. Finally, we apply these best practices to a legal case analysis scenario.
Differences Between O1/O3-mini and GPT-4o
Input Structure and Context Handling
- Built-in Reasoning vs. Prompted Reasoning: O1-series models have built-in chain-of-thought reasoning, meaning they internally reason through steps without needing explicit coaxing from the prompt. In contrast, GPT-4o often benefits from external instructions like “Let’s think step by step” to solve complex problems, since it doesn’t automatically engage in multi-step reasoning to the same extent. With O1/O3, you can present the problem directly; the model will analyze it deeply on its own.
- Need for External Information: GPT-4o has a broad knowledge base and access to tools (e.g. browsing, plugins, vision) in certain deployments, which helps it handle a wide range of topics. By comparison, the O1 models have a narrower knowledge base outside their training focus. For example, O1-preview excelled at reasoning tasks but couldn’t answer questions about itself due to limited knowledge context. This means when using O1/O3-mini, important background information or context should be included in the prompt if the task is outside common knowledge – do not assume the model knows niche facts. GPT-4o might already know a legal precedent or obscure detail, whereas O1 might require you to provide that text or data.
- Context Length: The reasoning models come with very large context windows. O1 supports up to 128k tokens of input, and O3-mini accepts up to 200k tokens (with up to 100k tokens output), exceeding GPT-4o’s context length. This allows you to feed extensive case files or datasets directly into O1/O3. For prompt engineering, structure large inputs clearly (use sections, bullet points, or headings) so the model can navigate the information. Both GPT-4o and O1 can handle long prompts, but O1/O3’s higher capacity means you can include more detailed context in one go, which is useful in complex analyses.
Reasoning Capabilities and Logical Deduction
- Depth of Reasoning: O1 and O3-mini are optimized for methodical, multi-step reasoning. They literally “think longer” before answering, which yields more accurate solutions on complex tasks. For instance, O1-preview solved 83% of problems on a challenging math exam (AIME), compared to GPT-4o’s 13% – a testament to its superior logical deduction in specialized domains. These models internally perform chain-of-thought and even self-check their work. GPT-4o is also strong but tends to produce answers more directly; without explicit prompting, it might not analyze as exhaustively, leading to errors in very complex cases that O1 could catch.
- Handling of Complex vs. Simple Tasks: Because O1-series models default to heavy reasoning, they truly shine on complex problems that have many reasoning steps (e.g. multi-faceted analyses, long proofs). In fact, on tasks requiring five or more reasoning steps, a reasoning model like O1-mini or O3 outperforms GPT-4 by a significant margin (16%+ higher accuracy). However, this also means that for very simple queries, O1 may “overthink.” Research found that on straightforward tasks (fewer than 3 reasoning steps), O1’s extra analytical process can become a disadvantage – it underperformed GPT-4 in a significant portion of such cases due to excessive reasoning. GPT-4o might answer a simple question more directly and swiftly, whereas O1 might generate unnecessary analysis. The key difference is O1 is calibrated for complexity, so it may be less efficient for trivial Q&A.
- Logical Deduction Style: When it comes to puzzles, deductive reasoning, or step-by-step problems, GPT-4o usually requires prompt engineering to go stepwise (otherwise it might jump to an answer). O1/O3 handle logical deduction differently: they simulate an internal dialogue or scratchpad. For the user, this means O1’s final answers tend to be well-justified and less prone to logical gaps. It will have effectively done a “chain-of-thought” internally to double-check consistency. From a prompt perspective, you generally don’t need to tell O1 to explain or check its logic – it does so automatically before presenting the answer. With GPT-4o, you might include instructions like “first list the assumptions, then conclude” to ensure rigorous logic; with O1, such instructions are often redundant or even counterproductive.
Response Characteristics and Output Optimization
- Detail and Verbosity: Because of their intensive reasoning, O1 and O3-mini often produce detailed, structured answers for complex queries. For example, O1 might break down a math solution into multiple steps or provide a rationale for each part of a strategy plan. GPT-4o, on the other hand, may give a more concise answer by default or a high-level summary, unless prompted to elaborate. In terms of prompt engineering, this means O1’s responses might be longer or more technical. You have more control over this verbosity through instructions. If you want O1 to be concise, you must explicitly tell it (just as you would GPT-4) – otherwise, it might err on the side of thoroughness. Conversely, if you want a step-by-step explanation in the output, GPT-4o might need to be told to include one, whereas O1 will happily provide one if asked (and has likely done the reasoning internally regardless).
- Accuracy and Self-Checking: The reasoning models exhibit a form of self-fact-checking. OpenAI notes that O1 is better at catching its mistakes during the response generation, leading to improved factual accuracy in complex responses. GPT-4o is generally accurate, but it can occasionally be confidently wrong or hallucinate facts if not guided. O1’s architecture reduces this risk by verifying details as it “thinks.” In practice, users have observed that O1 produces fewer incorrect or nonsensical answers on tricky problems, whereas GPT-4o might require prompt techniques (like asking it to critique or verify its answer) to reach the same level of confidence. This means you can often trust O1/O3 to get complex questions right with a straightforward prompt, whereas with GPT-4 you might add instructions like “check your answer for consistency with the facts above.” Still, neither model is infallible, so critical factual outputs should always be reviewed.
- Speed and Cost: A notable difference is that O1 models are slower and more expensive in exchange for their deeper reasoning. O1 Pro even includes a progress bar for long queries. GPT-4o tends to respond faster for typical queries. O3-mini was introduced to offer a faster, cost-efficient reasoning model – it’s much cheaper per token than O1 or GPT-4o and has lower latency. However, O3-mini is a smaller model, so while it’s strong in STEM reasoning, it might not match full O1 or GPT-4 in general knowledge or extremely complex reasoning. When prompt engineering for optimal response performance, you need to balance depth vs. speed: O1 might take longer to answer thoroughly. If latency is a concern and the task isn’t maximal complexity, O3-mini (or even GPT-4o) could be a better choice. OpenAI’s guidance is that GPT-4o “is still the best option for most prompts,” using O1 primarily for truly hard problems in domains like strategy, math, and coding. In short, use the right tool for the job – and if you use O1, anticipate longer responses and plan for its slower output (possibly by informing the user or adjusting system timeouts).
Prompt Engineering Techniques to Maximize Performance
Leveraging O1 and O3-mini effectively requires a slightly different prompting approach than GPT-4o. Below are key prompt engineering techniques and best practices to get the best results from these reasoning models:
Keep Prompts Clear and Minimal
Be concise and direct with your ask. Because O1 and O3 perform intensive internal reasoning, they respond best to focused questions or instructions without extraneous text. OpenAI and recent research suggest avoiding overly complex or leading prompts for these models. In practice, this means you should state the problem or task plainly and provide only necessary details. There is no need to add “fluff” or multiple rephrasing of the query. For example, instead of writing: “In this challenging puzzle, I’d like you to carefully reason through each step to reach the correct solution. Let’s break it down step by step...”, simply ask: “Solve the following puzzle [include puzzle details]. Explain your reasoning.” The model will naturally do the step-by-step thinking internally and give an explanation. Excess instructions can actually overcomplicate things – one study found that adding too much prompt context or too many examples worsened O1’s performance, essentially overwhelming its reasoning process. Tip: For complex tasks, start with a zero-shot prompt (just the task description) and only add more instruction if you find the output isn’t meeting your needs. Often, minimal prompts yield the best results with these reasoning models.
Avoid Unnecessary Few-Shot Examples
Traditional prompt engineering for GPT-3/4 often uses few-shot examples or demonstrations to guide the model. With O1/O3, however, less is more. The O1 series was explicitly trained to not require example-laden prompts. In fact, using multiple examples can hurt performance. Research on O1-preview and O1-mini showed that few-shot prompting consistently degraded their performance – even carefully chosen examples made them do worse than a simple prompt in many cases. The internal reasoning seems to get distracted or constrained by the examples. OpenAI’s own guidance aligns with this: they recommend limiting additional context or examples for reasoning models to avoid confusing their internal logic. Best practice: use zero-shot or at most one example if absolutely needed. If you include an example, make it highly relevant and simple. For instance, in a legal analysis prompt, you generally would not prepend a full example case analysis; instead, just ask directly about the new case. The only time you might use a demonstration is if the task format is very specific and the model isn’t following instructions – then show one brief example of the desired format. Otherwise, trust the model to figure it out from a direct query.
Leverage System/Developer Instructions for Role and Format
Setting a clear instructional context can help steer the model’s responses. With the API (or within a conversation’s system message), define the model’s role or style succinctly. For example, a system message might say: “You are an expert scientific researcher who explains solutions step-by-step”. O1 and O3-mini respond well to such role instructions and will incorporate them in their reasoning. However, remember that they already excel at understanding complex tasks, so your instructions should focus on what kind of output you want, not how to think. Good uses of system/developer instructions include:
- Defining the task scope or persona: e.g. “Act as a legal analyst” or “Solve the problem as a math teacher explaining to a student.” This can influence tone and the level of detail.
- Specifying the output format: If you need the answer in a structured form (bullet points, a table, JSON, etc.), explicitly say so. O1 and especially O3-mini support structured output modes and will adhere to format requests. For instance: “Provide your findings as a list of key bullet points.” Given their logical nature, they tend to follow format instructions accurately, which helps maintain consistency in responses
- Setting boundaries: If you want to control verbosity or focus, you can include something like “Provide a brief conclusion after the detailed analysis” or “Only use the information given without outside assumptions.” The reasoning models will respect these boundaries, and it can prevent them from going on tangents or hallucinating facts. This is important since O1 might otherwise produce a very exhaustive analysis – which is often great, but not if you explicitly need just a summary.
Ensure any guidance around tone, role, format is included each time.
Control Verbosity and Depth Through Instructions
While O1 and O3-mini will naturally engage in deep reasoning, you have control over how much of that reasoning is reflected in the output. If you want a detailed explanation, prompt for it (e.g. “Show your step-by-step reasoning in the answer”). They won’t need the nudge to do the reasoning, but they do need to be told if you want to see it. Conversely, if you find the model’s answers too verbose or technical for your purposes, instruct it to be more concise or to focus only on certain aspects. For example: “In 2-3 paragraphs, summarize the analysis with only the most critical points.” The models are generally obedient to such instructions about length or focus. Keep in mind that O1’s default behavior is to be thorough – it’s optimized for correctness over brevity – so it may err on the side of giving more details. A direct request for brevity will override this tendency in most cases.
For O3-mini, OpenAI provides an additional tool to manage depth: the “reasoning effort” parameter (low, medium, high). This setting lets the model know how hard to “think.” In prompt terms, if using the API or a system that exposes this feature, you can dial it up for very complex tasks (ensuring maximum reasoning, at the cost of longer answers and latency) or dial it down for simpler tasks (faster, more streamlined answers). This is essentially another way to control verbosity and thoroughness. If you don’t have direct access to that parameter, you can mimic a low effort mode by explicitly saying “Give a quick answer without deep analysis” for cases where speed matters more than perfect accuracy. Conversely, to mimic high effort, you might say “Take all necessary steps to arrive at a correct answer, even if the explanation is long.” These cues align with how the model’s internal setting would operate.
Ensure Accuracy in Complex Tasks
To get the most accurate responses on difficult problems, take advantage of the reasoning model’s strengths in your prompt. Since O1 can self-check and even catch contradictions, you can ask it to utilize that: e.g. “Analyze all the facts and double-check your conclusion for consistency.” Often it will do so unprompted, but reinforcing that instruction can signal the model to be extra careful. Interestingly, because O1 already self-fact-checks, you rarely need to prompt it with something like “verify each step” (that’s more helpful for GPT-4o). Instead, focus on providing complete and unambiguous information. If the question or task has potential ambiguities, clarify them in the prompt or instruct the model to list any assumptions. This prevents the model from guessing wrongly.
Handling sources and data: If your task involves analyzing given data (like summarizing a document or computing an answer from provided numbers), make sure that data is clearly presented. O1/O3 will diligently use it. You can even break data into bullet points or a table for clarity. If the model must not hallucinate (say, in a legal context it shouldn’t make up laws), explicitly state “base your answer only on the information provided and common knowledge; do not fabricate any details.” The reasoning models are generally good at sticking to known facts, and such an instruction further reduces the chance of hallucinationIterate and verify: If the task is critical (for example, complex legal reasoning or a high-stakes engineering calculation), a prompt engineering technique is to ensemble the model’s responses. This isn’t a single prompt, but a strategy: you could run the query multiple times (or ask the model to consider alternative solutions) and then compare answers. O1’s stochastic nature means it might explore different reasoning paths each time. By comparing outputs or asking the model to “reflect if there are alternative interpretations” in a follow-up prompt, you can increase confidence in the result. While GPT-4o also benefits from this approach, it’s especially useful for O1 when absolute accuracy is paramount – essentially leveraging the model’s own depth by cross-verifying.
Finally, remember that model selection is part of prompt engineering: If a question doesn’t actually require O1-level reasoning, using GPT-4o might be more efficient and just as accurate. OpenAI recommends saving O1 for the hard cases and using GPT-4o for the rest. So a meta-tip: assess task complexity first. If it’s simple, either prompt O1 very straightforwardly to avoid overthinking, or switch to GPT-4o. If it’s complex, lean into O1’s abilities with the techniques above.
How O1/O3 Handle Logical Deduction vs. GPT-4o
The way these reasoning models approach logical problems differs fundamentally from GPT-4o, and your prompt strategy should adapt accordingly:
- Internal Chain-of-Thought: O1 and O3-mini effectively perform an internal dialogue or step-by-step solution as they deduce answers. GPT-4o, unless explicitly guided, might not rigorously go through each step. For example, in a logic puzzle or a math word problem, GPT-4o might give a quick answer that sounds plausible but skips some reasoning, increasing the risk of error. O1 will automatically break the problem down, consider various angles, and only then give an answer, which is why it achieved dramatically higher scores on logic-heavy evaluations. Prompting difference: Do not prompt O1 to “show the reasoning” unless you actually want to see it. With GPT-4o, you’d use a CoT prompt (“First, think about... then ...”) to improve deduction, but with O1 this is built-in and telling it to do so externally can be redundant or even confusing. Instead, just ensure the problem is clearly stated and let O1 deductively reason it out.
- Handling Ambiguities: In logical deduction tasks, if there’s missing info or ambiguity, GPT-4o might make an assumption on the fly. O1 is more likely to flag the ambiguity or consider multiple possibilities because of its reflective approach. To leverage this, your prompt to O1 can directly ask: “If there are any uncertainties, state your assumptions before solving.” GPT-4 might need that nudge more. O1 might do it naturally or at least is less prone to assuming facts not given. So in comparing the two, O1’s deduction is cautious and thorough, whereas GPT-4o’s is swift and broad. Tailor your prompt accordingly – with GPT-4o, guide it to be careful; with O1, you mainly need to supply the information and let it do its thing.
- Step-by-Step Outputs: Sometimes you actually want the logical steps in the output (for teaching or transparency). With GPT-4o, you must explicitly request this (“please show your work”). O1 might include a structured rationale by default if the question is complex enough, but often it will present a well-reasoned answer without explicitly enumerating every step unless asked. If you want O1 to output the chain of logic, simply instruct it to — it will have no trouble doing so. In fact, O1-mini was noted to be capable of providing stepwise breakdowns (e.g., in coding problems) when prompted. Meanwhile, if you don’t want a long logical exposition from O1 (maybe you just want the final answer), you should say “Give the final answer directly” to skip the verbose explanation.
- Logical Rigor vs. Creativity: One more difference: GPT-4 (and 4o) has a streak of creativity and generative strength. Sometimes in logic problems, this can lead it to “imagine” scenarios or analogies, which isn’t always desired. O1 is more rigor-focused and will stick to logical analysis. If your prompt involves a scenario requiring both deduction and a bit of creativity (say, solving a mystery by piecing clues and adding a narrative), GPT-4 might handle the narrative better, while O1 will strictly focus on deduction. In prompt engineering, you might combine their strengths: use O1 to get the logical solution, then use GPT-4 to polish the presentation. If sticking to O1/O3 only, be aware that you might need to explicitly ask it for creative flourishes or more imaginative responses – they will prioritize logic and correctness by design.
Key adjustment: In summary, to leverage O1/O3’s logical strengths, give them the toughest reasoning tasks as a single well-defined prompt. Let them internally grind through the logic (they’re built for it) without micromanaging their thought process. For GPT-4o, continue using classic prompt engineering (decompose the problem, ask for step-by-step reasoning, etc.) to coax out the same level of deduction. And always match the prompt style to the model – what confuses GPT-4o might be just right for O1, and vice versa, due to their different reasoning approaches.
Crafting Effective Prompts: Best Practices Summary
To consolidate the above into actionable guidelines, here’s a checklist of best practices when prompting O1 or O3-mini:
- Use Clear, Specific Instructions: Clearly state what you want the model to do or answer. Avoid irrelevant details. For complex questions, a straightforward ask often suffices (no need for elaborate role-play or multi-question prompts).
- Provide Necessary Context, Omit the Rest: Include any domain information the model will need (facts of a case, data for a math problem, etc.), since the model might not have up-to-date or niche knowledge. But don’t overload the prompt with unrelated text or too many examples – extra fluff can dilute the model’s focus
- Minimal or No Few-Shot Examples: By default, start with zero-shot prompts. If the model misinterprets the task or format, you can add one simple example as guidance, but never add long chains of examples for O1/O3. They don’t need it, and it can even degrade performance.
- Set the Role or Tone if Needed: Use a system message or a brief prefix to put the model in the right mindset (e.g. “You are a senior law clerk analyzing a case.”). This helps especially with tone (formal vs. casual) and ensures domain-appropriate language.
- Specify Output Format: If you expect the answer in a particular structure (list, outline, JSON, etc.), tell the model explicitly. The reasoning models will follow format instructions reliably. For instance: “Give your answer as an ordered list of steps.”
- Control Length and Detail via Instructions: If you want a brief answer, say so (“answer in one paragraph” or “just give a yes/no with one sentence explanation”). If you want an in-depth analysis, encourage it (“provide a detailed explanation”). Don’t assume the model knows your desired level of detail by default – instruct it.
- Leverage O3-mini’s Reasoning Effort Setting: When using O3-mini via API, choose the appropriate reasoning_effort (low/medium/high) for the task
- . High gives more thorough answers (good for complex legal reasoning or tough math), low gives faster, shorter answers (good for quick checks or simpler queries). This is a unique way to tune the prompt behavior for O3-mini.
- Avoid Redundant “Think Step-by-Step” Prompts: Do not add phrases like “let’s think this through” or chain-of-thought directives for O1/O3; the model already does this internally. Save those tokens and only use such prompts on GPT-4o, where they have impact. An exception might be if you explicitly want the model to output each step for transparency – then you can ask for that in the output, but you still don’t need to tell it to actually perform reasoning.
- Test and Iterate: Because these models can be sensitive to phrasing, if you don’t get a good answer, try rephrasing the question or tightening the instructions. You might find that a slight change (e.g. asking a direct question vs. an open-ended prompt) yields a significantly better response. Fortunately, O1/O3’s need for iteration is less than older models (they usually get complex tasks right in one go), but prompt tweaking can still help optimize clarity or format.
- Validate Important Outputs: For critical use-cases, don’t rely on a single prompt-answer cycle. Use follow-up prompts to ask the model to verify or justify its answer (“Are you confident in that conclusion? Explain why.”), or run the prompt again to see if you get consistent results. Consistency and well-justified answers indicate the model’s reasoning is solid.
By following these techniques, you can harness O1 and O3-mini’s full capabilities and get highly optimized responses that play to their strengths.
Applying Best Practices to a Legal Case Analysis
Finally, let’s consider how these prompt engineering guidelines translate to a legal case analysis scenario (as mentioned earlier). Legal analysis is a perfect example of a complex reasoning task where O1 can be very effective, provided we craft the prompt well:
- Structure the Input: Start by clearly outlining the key facts of the case and the legal questions to be answered. For example, list the background facts as bullet points or a brief paragraph, then explicitly ask the legal question: “Given the above facts, determine whether Party A is liable for breach of contract under U.S. law.” Structuring the prompt this way makes it easier for the model to parse the scenario. It also ensures no crucial detail is buried or overlooked.
- Provide Relevant Context or Law: If specific statutes, case precedents, or definitions are relevant, include them (or summaries of them) in the prompt. O1 doesn’t have browsing and might not recall a niche law from memory, so if your analysis hinges on, say, the text of a particular law, give it to the model. For instance: “According to [Statute X excerpt], [provide text]… Apply this statute to the case.” This way, the model has the necessary tools to reason accurately.
- Set the Role in the System Message: A system instruction like “You are a legal analyst who explains the application of law to facts in a clear, step-by-step manner.” will cue the model to produce a formal, reasoned analysis. While O1 will already attempt careful reasoning, this instruction aligns its tone and structure with what we expect in legal discourse (e.g. citing facts, applying law, drawing conclusions).
- No Need for Multiple Examples: Don’t supply a full example case analysis as a prompt (which you might consider doing with GPT-4o). O1 doesn’t need an example to follow – it can perform the analysis from scratch.. You might, however, briefly mention the desired format: “Provide your answer in an IRAC format (Issue, Rule, Analysis, Conclusion).” This format instruction gives a template without having to show a lengthy sample, and O1 will organize the output accordingly.
- Control Verbosity as Needed: If you want a thorough analysis of the case, let O1 output its comprehensive reasoning. The result may be several paragraphs covering each issue in depth. If you find the output too verbose or if you specifically need a succinct brief (for example, a quick advisory opinion), instruct the model: “Keep the analysis to a few key paragraphs focusing on the core issue.” This ensures you get just the main points. On the other hand, if the initial answer seems too brief or superficial, you can prompt again: “Explain in more detail, especially how you applied the law to the facts.” O1 will gladly elaborate because it has already done the heavy reasoning internally.
- Accuracy and Logical Consistency: Legal analysis demands accuracy in applying rules to facts. With O1, you can trust it to logically work through the problem, but it’s wise to double-check any legal citations or specific claims it makes (since its training data might not have every detail). You can even add a prompt at the end like, “Double-check that all facts have been addressed and that the conclusion follows the law.” Given O1’s self-checking tendency, it may itself point out if something doesn’t add up or if additional assumptions were needed. This is a useful safety net in a domain where subtle distinctions matter.
- Use Follow-Up Queries: In a legal scenario, it’s common to have follow-up questions. For instance, if O1 gives an analysis, you might ask, “What if the contract had a different clause about termination? How would that change the analysis?” O1 can handle these iterative questions well, carrying over its reasoning. Just remember that, if the project you ar working on, the interface doesn’t have long-term memory beyond the current conversation context (and no browsing), each follow-up should either rely on the context provided or include any new information needed. Keep the conversation focused on the case facts at hand to prevent confusion.
By applying these best practices, your prompts will guide O1 or O3-mini to deliver high-quality legal analysis. In summary, clearly present the case, specify the task, and let the reasoning model do the heavy lifting. The result should be a well-reasoned, step-by-step legal discussion that leverages O1’s logical prowess, all optimized through effective prompt construction.
Using OpenAI’s reasoning models in this way allows you to tap into their strength in complex problem-solving while maintaining control over the style and clarity of the output. As OpenAI’s own documentation notes, the O1 series excels at deep reasoning tasks in domains like research and strategy– legal analysis similarly benefits from this capability. By understanding the differences from GPT-4o and adjusting your prompt approach accordingly, you can maximize the performance of O1 and O3-mini and obtain accurate, well-structured answers even for the most challenging reasoning tasks.
Published Feb 05, 2025
Version 1.0Agustinmantaras
Microsoft
Joined March 02, 2024
AI - Azure AI services Blog
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