# OpenAI's new "o1" Model: When to Use It and When Not to
Source: https://humanspark.ai/openai-o1-model/

OpenAI's o1 was the first of a new kind of model - one that reasons before it answers. The specifics here are a September 2024 snapshot, but the decision it forces is evergreen: when is slow, careful reasoning worth it, and when do you want fast and cheap instead?

> o1's "chain-of-thought" reasoning breaks complex problems into smaller steps, working through them like a human expert.

That makes it excellent for detailed, multi-step problems - and overkill for plenty of everyday tasks. Here's how I decide, after testing it on real work.

## 01The test: feedback on a book manuscript

I gave o1 two full chapters of my book and asked for line-by-line editorial feedback. It thought for 62 seconds before replying (unusually long - most responses came back in 5-10 seconds).

// what "thinking for 62 seconds" looked like

A glimpse of its visible reasoning: *"Reading and analysing... mapping the request to OpenAI's policies... noting the engaging anecdotes and conversational tone... combining sentences for a more cohesive introduction... reducing redundancy and a grammatical issue... ensuring a clean, engaging storytelling flow."* Dozens of steps like these, then a structured set of specific, located suggestions.

> The feedback was incredibly detailed, and genuinely useful - I implemented several of the suggestions.

## 02Where o1 earns its slowness

The reasoning models shine when an expert *could* solve it - just not quickly.

use it for

Complex problem-solving

Step-by-step challenges: debugging multi-step code, large sequential problems like physics or health-data analysis.

use it for

Creative problem-solving

Working iteratively through options - product development, long-term strategy - testing approaches before committing.

use it for

Education and tutoring

It shows the reasoning, not just the answer - breaking a problem into steps and explaining each, which is where the learning is.

use it for

High-stakes fields

Where mistakes are costly - healthcare, aviation - the careful, step-by-step approach is worth the extra time.

## When to stick with a faster model

For these, GPT-4o or Claude 3.5 Sonnet are the better call - speed beats depth.

Quick answers

FAQs, general Q&A, basic info - fast, without deep reasoning.

Content & marketing

Blog posts, copy, social - creative, flowing text generated quickly.

Conversational AI

Chatbots and assistants - smoother real-time dialogue, no reasoning delay.

Summaries

Pulling key points from a long document, light data analysis.

Time-sensitive

Live customer chats and anything where fast, real-time answers matter most.

## 03How to choose

Is the task complex and multi-step?

Go with a reasoning model - it's built for careful, step-by-step work.

Do you need speed?

Use a fast model. Most people won't reach for a reasoning model for everyday tasks.

Are the stakes high?

The slow, careful approach is worth the wait when accuracy is essential.

Need creativity or conversation?

Stick with a fast model for content and smooth dialogue.

Usage limits (Sept 2024 snapshot)

OpenAI capped o1-preview at 30 messages a week (50 for o1-mini) on Plus/Team. Limits like these have eased over time, but reasoning models remain more expensive and rate-limited than fast models - plan accordingly.

Reasoning models are for problems an expert could solve - just not quickly.

For everything else - speed, creativity, conversation - a fast model wins. Match the model to the job.

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