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Choosing a real big linguistic model may feel overwhelming with so many options outside, especially if you don’t live and breathe AI
But as we worked through each, we got a real feeling what they are good at (and where they fall).
So let’s talk about what to use when.
Chatgpt & Openi-O1: Reliable comprehensive
Let’s start with chatgpt and open O1.
The latest Openi model is impressive, and people are covered with their “reasoning” skills, it is designed to deal with more logical things with creative tasks in which Chatgpt has always been great.
Why do we like it
- Big on logic: Openi-O1 uses something called a chain explanation. To put it simply, it is better to walk through complex problems step by step.
- Adapted GPT -ovi: This feature allows us to create a model that remembers the instructions specific to our work. If we need to think as a project manager or social media assistant, we can set it with only a few clicks.
Where it falls briefly
- Overkill for basic things: Most of the time GPT-4 can do a job. Openi-O1 shines with complex tasks, but you may not notice a huge difference for more direct cases of use.
- It’s not a quantum jump: Great improvements are behind the scenes. If you expect you to see huge changes in your daily use, you may be insidious.
When to use it: Anything involving a more complex logic or when you need custom answers, such as encoding or detailed content editing.
Claude by Anthropic: Sumrizer and narrative champion
Claude is our movements to summarize and the meaning of long documents.
It is also fantastic in storytelling, which is useful if you are creating content or you need to simplify thick information.
What makes it stand out
- Summary of documents: Claude is amazing in the key information, so it is perfect when we have huge documents M and we need a quick summary.
- Adjusting custom customers: Anthropic project feature allows us to set customized instructions for repeated tasks. It feels more intuitive than Chatgpt.
What to look out for
- File size limit: If you transfer a large file (over 20 MB), Claude sometimes throws a fit. We usually compress the PDF to work on it, but it’s worth knowing.
The case best use: Summary or creating content when you need a simple, reliable tool that is easy to move with.
Google Gemini: King of Context (and Podcasting)
Google twins feel like it is in their league when it comes to handling the tone of data.
We like to have a huge window context window, which means that it can hold and process all of the books if necessary. In addition, it has a strange new tool called Notebook LM, which transforms documents into a mini-subtle for you.
Why is he cool
- Processes huge data loads: With a limit of 10 million words, twins can follow huge documents at once, so we can load all of the libraries if we need.
- Notebook lm: This feature actually turns documents into sound summaries in a conversational podcast format. It’s a great way to get something on more tasks.
Disadvantages
- Limited adjustment: Although he has “gems” (Google’s response to the custom GPTS), they are quite basic. You can’t connect it to other tools or APIs as you can with Chatgpt or Claude.
When to turn to twins: When you need to process the mountain of data at once or if you are in the mood for a summary of sound while doing something else.
Meta llam: Privacy and flexibility
The llam is not necessarily the most advanced, but given that it is open, it is our thing when privacy is concerned.
Unlike the others, the llam can escape off the net on your computer so that it does not share the data with a large technological company.
Why would I recommend it
- Keeps things private: Because Llam works locally, we can be sure that our data stay out of the internet.
- Very adaptable: Llama’s open source, which means that we (or any developer) can change it for unique needs. We don’t do that much, but it’s nice to know it’s an option.
Weak points
- Not the most powerful: Not as good as Claude or Chatgpt for high quality content or problem solving. But for basic cases of use, it is firm.
When it makes sense to use: Anytime privacy is key, like sensitive internal data or when you just need a fast local solution.
Grok by XAI: Twitter Data and Realist Generation of Pictures
Grok is fun – this is originally from social media, integrated with x (earlier twitter).
It is a decent model and comes with a strong picture generator, a Flux One, who can make a super realistic visual. But where it really shines, in real time it pulls Twitter data.
Why we use it
- Live seeing twitter: Grok allows us to see what’s in trend or analyzing popular profiles on Twitter on the spot.
- To generate pictures: Flux one can create realistic images of people, scenes and more, with a few limitations to topics.
Lack
- Cases of niche use: It is great for data and pictures on Twitter, but they do not stand out in general tasks such as abstract or narrative.
Ideal use: Social Media Research and generating realistic visuals for content.
Confusion: The best friend of the researcher
The confusion technically is not a llm in the traditional sense. Instead, it is a research tool with AI drive that pulls information from the Internet and then uses the model to organize.
This is our movements when I need fast, accurate information or other opinion on a topic.
What makes it neophod
- Web options: Confusion searches the Internet and summarizes the content, which makes it perfect for heavy research tasks.
- Choose your model: We can use GPT-4, Claude or even Openi-O1 as your “engine” in confusion, so we always get a model that fits our needs.
Warning
- Twice check for accuracy: Sometimes mixes similar names or withdraws outdated information, so it is good to cross the important facts.
When I use confusion: Whenever I’m in a “Research mode” or I need updated insights for blog posts, presentations or meetings.
Finding a real llm can be as simple as the tool strength with your needs.
Our advice? Try a few and do not hesitate to mix and match to achieve the best results.
