ChatGPT Review 2025: The AI Assistant That Changed Everything

What ChatGPT Is in 2025

ChatGPT is an AI assistant made by OpenAI. You probably already know that. Most people reading a review of ChatGPT in 2025 have already used it at least a few times. So rather than spending paragraphs explaining what a large language model is, I want to talk about what ChatGPT is like to use every day, what it is actually good at, where it still lets you down, and whether the paid version is worth twenty dollars a month when the free version keeps getting better.

I have been using ChatGPT daily for about five months now. Sometimes for writing. Sometimes for coding. Sometimes for answering questions I am too lazy to Google properly. And sometimes just to think through problems out loud with something that responds in a way that feels eerily like talking to a smart friend who happens to know everything about every topic. (Except the things it confidently gets wrong. We will get to that.)

The Conversational Experience

The core thing ChatGPT does -- holding a conversation -- has gotten remarkably good. GPT-4o, which is the model most people interact with, handles multi-turn conversations with a fluency and contextual awareness that would have been science fiction five years ago. You can start a conversation about a JavaScript bug, pivot to asking about the best way to cook salmon, then come back to the bug, and it keeps track of all of it. The context window is large enough that you can have genuinely long conversations without the AI "forgetting" what you discussed earlier.

The quality of reasoning has improved a lot from the early GPT-3.5 days. It handles nuance better. It is less likely to give you a generic non-answer when you ask something complicated. And the o1 model (available on Plus) takes things further by actually "thinking" before responding -- it shows a chain of reasoning for complex math, coding, and science problems. The difference between GPT-4o and o1 on a tricky logic puzzle is noticeable. o1 gets it right more often, but it is slower, so there is a trade-off.

That said, it still has this maddening tendency to be confidently wrong about specific facts. I asked it about the founding date of a relatively obscure tech company last week and it gave me the wrong year with zero hedging. No "I think" or "I am not certain." Just stated it like gospel. This has gotten better over the versions -- it happens less often -- but when it happens, it is annoying precisely because the AI sounds so sure of itself. You cannot fully trust it on factual claims without checking. That is the deal you make with every LLM right now, and ChatGPT is no exception.

Where ChatGPT Genuinely Shines

This is the long section because honestly, there is a lot to cover here. ChatGPT went from "neat chatbot" to "I use this more than Google" over the course of about a year, and the reasons are worth unpacking.

Writing assistance is probably where I get the most value. Not generating entire articles (I write my own stuff, thanks) but the in-between tasks. Drafting emails where I know what I want to say but cannot find the right tone. Rewriting a paragraph that sounds clunky. Summarizing a 20-page PDF into three bullet points for a meeting. Generating five alternative subject lines for a newsletter and picking the best one. Turning my rambling notes into a coherent outline. These tasks used to eat up scattered minutes throughout the day. Now they take seconds. Cumulatively, that adds up to maybe an hour saved per week. Not life-changing. But noticeable.

Coding help is where ChatGPT has become almost indispensable for me. I write Python and JavaScript mostly, and ChatGPT is like having a very patient senior developer sitting next to me who never judges when I ask basic questions. It explains error messages in plain English. It generates boilerplate code that is 80% right and needs minor tweaking. It refactors messy functions into cleaner versions. It writes unit tests (which I should write myself but let us be real, nobody writes enough tests). And when I am working in an unfamiliar framework or language, it teaches as it helps, which makes me faster the next time. The o1 model is particularly good at complex coding problems -- multi-step algorithm design, tricky debugging, performance optimization -- where the extra "thinking time" produces better solutions than GPT-4o.

Code Interpreter (now called Advanced Data Analysis) is the feature that turned me from a casual user into a paying subscriber. Upload a CSV of sales data and ChatGPT writes Python to analyze it, calculates statistics, generates charts, identifies trends, and explains everything in plain language. I am not a data analyst. I do not want to learn pandas. But I have data I need to understand, and Code Interpreter bridges that gap in a way that nothing else does. I used it last week to analyze three months of website traffic data and it found a pattern I had not noticed -- a specific day of the week was consistently underperforming, which turned out to correlate with when we were publishing new content. That insight would have taken me hours to find manually. Code Interpreter found it in about 45 seconds.

The multimodal stuff -- uploading images, voice conversations, file analysis -- has gone from "cool demo" to actually useful. I took a photo of an error message on my colleague's screen (faster than typing it out) and ChatGPT diagnosed the issue. I uploaded a screenshot of a complex spreadsheet and asked it to explain what the formulas were doing. I took a picture of a restaurant menu in Japanese and got a translation with dish descriptions. Voice mode is great when I am walking or driving and want to think through an idea out loud -- it responds naturally, you can interrupt it, and the conversation feels surprisingly organic. Not quite like talking to a person. But not far off.

Custom GPTs are cool in theory and mixed in practice. The idea is you can create a specialized version of ChatGPT with specific instructions, reference documents, and tool access. I built one that acts as our company's style guide checker -- you paste in text and it flags deviations from our writing standards. That works well. I built another that is supposed to help with customer support scripts and it was... fine. The GPT Store has thousands of custom GPTs from other users but honestly, most of them are simple prompt wrappers that you could recreate in two minutes. The really useful ones are the ones you build yourself for your specific needs.

Canvas is the newer editing interface for longer documents and code. Instead of the back-and-forth chat where each response replaces the last, Canvas opens a side panel where your document lives and you can ask ChatGPT to edit specific sections. Select a paragraph, say "make this more concise," and it rewrites just that paragraph. It is a big improvement for long-form writing and coding projects where the standard chat interface gets unwieldy. I used it to write and refine a 3,000-word report and the workflow was noticeably smoother than the old approach of pasting and re-pasting in the chat.

The Pricing Situation

Free tier: you get GPT-4o mini and limited access to GPT-4o, plus web browsing, Code Interpreter, file uploads, and DALL-E image generation. Honestly? It is shockingly capable for zero dollars. If you use ChatGPT a few times a week for quick questions and light writing help, the free tier might be all you need. They have made it much more generous than it used to be.

Plus at $20 per month: higher message limits on GPT-4o, access to the o1 and o3-mini reasoning models, advanced voice mode, Canvas, Memory (remembers things about you across conversations), and early access to new features. This is the tier I am on. For daily use, it pays for itself easily. The higher GPT-4o limits alone are worth it if you use ChatGPT regularly -- on the free tier I was hitting caps by mid-afternoon on busy days.

Pro at $200 per month: unlimited everything, o1 Pro mode for the most intensive reasoning tasks. Two hundred bucks. That is a lot. I cannot justify it for my use case. But if you are a researcher processing hundreds of complex queries a day or a developer using o1 for architecture decisions on production systems, maybe the math works out. I have not tried it and probably will not unless my usage changes dramatically.

Team at $25 per user per month adds shared workspaces and admin controls. If your company is going to pay for AI tools for employees, this makes more sense than individual Plus subscriptions because of the management features. Enterprise is custom pricing with SSO, analytics, and all the usual enterprise stuff.

The Competition Is Getting Interesting

Claude (Anthropic) is the one I keep going back and forth on. Claude writes better prose than ChatGPT -- more natural, less formulaic, fewer of those telltale AI writing patterns. Its 200K token context window is massive compared to ChatGPT's, which makes it better for analyzing long documents. And it has Artifacts, which is like a sandbox for generating code, documents, and visualizations interactively. But ChatGPT has broader tool support (web browsing, Code Interpreter, DALL-E), more platform reach (the desktop and mobile apps are excellent), and the custom GPT ecosystem. For writing and long-document analysis, I actually prefer Claude. For everything else, ChatGPT.

Google Gemini has the Google integration advantage. It can pull from your Gmail, Docs, Drive, Calendar -- which is powerful if you live in the Google ecosystem. The actual response quality has gotten a lot better but in my testing it still falls slightly behind ChatGPT and Claude for nuanced tasks. The $20/month Gemini Advanced plan includes 2TB of storage though, which is a nice perk.

Perplexity is honestly better for research. Every answer cites its sources. You can see exactly where information came from and verify it. For factual questions where accuracy matters and you need receipts, Perplexity wins. But it cannot write code for you, it does not do image generation, and it is narrower in scope. Different tools for different jobs.

The Stuff That Bugs Me

Hallucinations. Still. In 2025. Less frequent than before, yes. But they still happen and they are still a problem because the AI does not flag its own uncertainty. It just states things. And some of those things are wrong. For high-stakes work -- legal, medical, financial, academic -- you absolutely cannot trust ChatGPT's output without verification. For casual use it is fine. But the line between "casual" and "I should probably check this" is blurry.

The message limits on Plus can be frustrating on heavy use days. I have hit the GPT-4o cap in the afternoon and had to switch to GPT-4o mini, which is noticeably worse for complex tasks. The limits have gotten more generous over time but they are still there, and on a day where I am deep in a coding project and going back and forth rapidly with the AI, they bite.

Privacy. This is the elephant. Your conversations are sent to OpenAI's servers. They say they do not train on your data if you opt out (and you can opt out). But you are still sending potentially sensitive information to a third party. For personal use, I am okay with this. For company data, it is a conversation worth having with your security team. The Team and Enterprise plans have stronger data handling policies but they cost more.

And look, the response quality is still prompt-dependent. Ask a vague question, get a vague answer. Ask a detailed question with context and constraints, get a much better answer. There is a skill to "prompting" that should not be necessary -- you should be able to just talk to it -- but in practice, the way you phrase things still matters a lot. It is getting better about this. But it is not there yet.

Who This Is For

Basically everyone? That sounds like a cop-out answer but it is kind of true. Knowledge workers who write emails and documents. Developers who code. Students who study. Researchers who research. Small business owners who need help with marketing copy. Content creators who need ideas. Data analysts who need charts without writing Python. Parents who need to explain photosynthesis to a seven-year-old at 9 PM. The use cases are genuinely broad.

It is less useful if your work is primarily physical, if you work in a field where AI outputs could be dangerous without expert review (medicine, law), or if your employer prohibits AI tools for security reasons. And if you are someone who cares deeply about knowing where information comes from and being able to trace claims to sources, Perplexity might serve you better.

I dunno. It is weird reviewing a tool that might be obsolete in a year or might be ten times better. The pace of change in AI is so fast that any review is basically a snapshot. This is what ChatGPT is like in early 2025. By the time you read this it might already have new features I have not tried. That is kind of the nature of reviewing something that updates every few weeks.

I keep using it though. Every day. So that probably tells you something.