o1-preview

Snapshot Verdict

The o1-preview model represents a fundamental shift in how large language models (LLMs) operate. By incorporating a "Chain of Thought" reasoning process before generating an answer, it tackles complex problems in logic, coding, and mathematics that have notoriously stumped previous models. While it is slower and more expensive than its predecessors, its ability to self-correct and think through multi-step problems makes it an indispensable tool for technical power users, even if it remains overkill for basic writing tasks.

Product Version

Version reviewed: o1-preview (Release September 2024)

What This Product Actually Is

OpenAI o1-preview is the first in a new series of AI models designed for advanced reasoning. Unlike the GPT-4 series, which predicts the next token in a sequence almost instantaneously, o1-preview uses reinforcement learning to "think" before it speaks. When you submit a prompt, the model generates a hidden internal chain of thought. It breaks down complex instructions, tries different approaches, and identifies its own mistakes before presenting the final output to the user.

This is not a direct replacement for GPT-4o. It lacks several multimodal features in its current preview state—such as file uploads, web browsing, and image generation—and it is significantly slower. Instead, it is a specialized tool optimized for STEM (Science, Technology, Engineering, and Mathematics), advanced programming, and difficult logic puzzles. It is essentially the "slow thinking" counterpart to the "fast thinking" of previous models.

Real-World Use & Experience

Using o1-preview feels different from using any previous AI. When you hit enter, you don’t get an immediate stream of text. Instead, you see a status indicator that says "Thinking." This can last anywhere from five seconds to over a minute depending on the complexity of the query. You can click on this indicator to see a summarized version of what the model is considering, which provides a fascinating window into its problem-solving process.

In practice, this extra time translates to higher accuracy in specific areas. For example, if you ask a standard LLM to write a complex Python script involving multiple API integrations and edge cases, it often hallucinates a function or misses a logic step. The o1-preview model tends to catch these errors during its "thinking" phase. It writes the code, realizes a specific library won't work that way, and rewrites it before you ever see the result.

However, for everyday tasks like drafting an email or summarizing a short article, the experience is frustrating. The delay feels unnecessary, and the output quality for creative writing is often more clinical and less "human" than GPT-4o. It is a clinical instrument, not a creative partner.

Standout Strengths

• Exceptional performance in complex mathematics.
• High-level architectural coding and debugging.
• Superior logical reasoning and puzzle solving.

The most impressive aspect of o1-preview is its performance on PhD-level science questions and competitive programming. In benchmarks provided by OpenAI—which reflect real-world testing—the model placed in the 89th percentile on Codeforces competitive programming questions. For a professional developer, this means the model can handle deeper architectural questions rather than just repetitive boilerplate code.

The reasoning capabilities also extend to legal and medical logic. It can parse through contradictory sets of rules or complex diagnostic criteria with a level of nuance that previously required multiple rounds of prompting and "chain of thought" engineering by the user. With o1-preview, the "chain of thought" is built-in and automated.

Finally, the model’s ability to follow complex, multi-layered instructions is a significant leap forward. If you give it a prompt with ten different constraints, it is much more likely to respect every single one of them compared to GPT-4o, which often "forgets" constraints halfway through a long response.

Limitations, Trade-offs & Red Flags

• Significantly slower response times.
• Limited feature set (no browsing/files).
• High cost and strict usage limits.

The most immediate red flag is the speed. In a world of "instant" AI, waiting 30 seconds for a response feels like an eternity. If your workflow requires rapid-fire iteration, o1-preview will feel like a bottleneck.

There is also a significant "reasoning tax" in terms of usage limits. Since the model consumes more compute power to think, OpenAI has historically placed much lower message caps on o1-preview compared to GPT-4o. This makes it a tool you save for the "hard problems" rather than something you use for your entire workday.

Another limitation is the lack of "world knowledge" tools. Because the preview version (at the time of writing) often lacks live web browsing and the ability to analyze uploaded documents or images, its utility is confined to what is in its training data and what you can fit in the text prompt. It cannot help you analyze a 50-page PDF or check today's news.

Lastly, there is the "hidden" nature of its thought process. While you see a summary of the reasoning, the full raw chain of thought is hidden from the user. This is a deliberate safety and competitive choice by OpenAI, but it means you can't always see exactly where it went wrong if it eventually produces an error.

Who It's Actually For

This model is specifically for people who deal with high-logic, high-consequence data.

If you are a software engineer building complex systems, o1-preview can help you map out logic that would usually require a senior peer review. If you are a physicist, mathematician, or data scientist, this model can assist with symbolic math and formula derivation in a way that previous models could not.

It is also an excellent tool for "Prompt Engineers" or those building AI agents. Because o1-preview is so good at following instructions, it can be used to generate the prompts and logic flows that simpler models will execute.

It is NOT for the casual user who wants to write a poem, generate a recipe from a photo of their fridge, or summarize a meeting transcript. For those tasks, GPT-4o is faster, cheaper, and more capable.

Value for Money & Alternatives

Currently, o1-preview is included in ChatGPT Plus, Team, and Enterprise subscriptions. It is not available to free users. Since it sits alongside GPT-4o, the value is essentially a "bonus" for existing subscribers. However, as an API product, it is significantly more expensive than GPT-4o (often 3x to 4x the price per million tokens).

For the average professional, the value is fair because it solves problems that previously required human intervention. If it saves a developer two hours of debugging, it has paid for the monthly subscription ten times over. If you aren't doing technical work, the value is poor because you are paying for "thinking" time you don't actually need.

Value for money: fair

Alternatives

• Claude 3.5 Sonnet — Faster and often better at nuanced, natural-sounding prose while remaining very strong at coding.
• GPT-4o — The better "all-around" tool for daily tasks, image analysis, and web browsing.
• DeepSeek-V3 — A strong competitor in the reasoning space that often provides high-level coding assistance at a lower price point.

Final Verdict

The o1-preview model is a specialist tool, not a generalist expansion. It marks the end of the "hallucination-heavy" era for technical tasks and the beginning of the "deliberative AI" era. If you treat it like a search engine or a chatbot, you will be disappointed by its speed and lack of features. If you treat it like a junior developer or a research assistant, it will prove to be one of the most powerful tools in your digital arsenal. It is a glimpse into the future of AI where "logic" is just as important as "language."