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- Cloud 3 Surpasses GPT-4 on Every Benchmark - Full Breakdown and Testing
Cloud 3 Surpasses GPT-4 on Every Benchmark - Full Breakdown and Testing
Discover how Cloud 3 outperforms GPT-4 in every aspect with a detailed breakdown and testing. Learn about its features, models, and how it could potentially be the next GPT-4 killer in creative writing. Stay till the end for new benchmark questions and crucial insights.
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Video Summary & Chapters
1. Introduction to Cloud 3
Overview of Cloud 3 and its benchmark performance compared to GPT-4.
2. Cloud 3 Model Variants
Explanation of the different Cloud 3 models - Haiku, Sonnet, and Opus - and their respective uses.
3. Choosing the Right Model
Guidance on selecting the appropriate Cloud 3 model based on use cases and needs.
4. Advanced Capabilities of Cloud 3
Exploration of Cloud 3's enhanced capabilities in various tasks like code generation and multilingual conversations.
5. Benchmark Performance Comparison
Comparison of Cloud 3 models with GPT-4 across multiple benchmarks, showcasing superior performance.
6. Real-Time Applications
Discussion on Cloud 3's ability to power live customer chats and immediate response tasks.
7. Enhancements in Sonnet Model
Highlighting the improvements and speed of the Sonnet model in Cloud 3 for rapid response tasks and visual processing.
8. Contextual Understanding Improvements
Enhancements in reducing model refusals and contextual understanding
9. Accuracy and Performance Comparison
Comparison of output accuracy and performance between Cloud 3 and Cloud 2.1
10. Extended Context Window
Discussion on the large context window capabilities of Cloud models
11. Needle in the Haystack Test
Exploring model accuracy in identifying hidden question-answer pairs
12. Usability and Functionality
Ease of use and functionality improvements in Cloud 3 model
13. Pricing and Model Comparison
Comparison of pricing and capabilities across different Cloud models
14. Use Cases for Different Models
Exploring potential use cases based on model sizes and capabilities
15. Cost Analysis and Use Case Complexity
Analysis of pricing based on model capabilities and complexity
16. Performance Testing Cloud 3 Opus vs. GPT-4 Turbo
Comparative performance testing between Cloud 3 Opus and GPT-4 Turbo
17. Cloud 3 vs. GPT-4
Comparison of Cloud 3 Opus and GPT-4 models on various benchmarks.
18. Python Script Output Test
Testing the speed and accuracy of Python script output by Cloud 3 and GPT-4.
19. Snake Game Creation
Creating and testing the snake game in Python using Cloud 3 and GPT-4.
20. Snake Game Testing
Testing the functionality and performance of the snake game output by Cloud 3 and GPT-4.
21. Censorship Test
Examining how Cloud 3 and GPT-4 handle censored queries and responses.
22. Shirt Drying Problem
Solving the shirt drying problem and comparing the reasoning of Cloud 3 and GPT-4.
23. Upside Down Cup Experiment 🥤
Testing the marble inside the upside-down cup.
24. Logic and Reasoning Puzzle 🤔
John and Mark's ball placement scenario.
25. Word Ending Challenge 🍎
10 sentences ending with the word 'apple' test.
26. Model Comparison Analysis 🤖
Analyzing Claude 3 and GPT-4 performance differences.
27. Digging Time Dilemma ⏳
Exploring the time taken by multiple people to dig a hole.
28. Final Thoughts and Comparison 👑
Comparing Cloud3 Opus and GPT-4 performance.
Video Transcript
Cloud 3 was just released today and by their accounts and benchmarks, it beats GPD 4 across the board.
So I'm going to tell you everything about it, then we're going to test it out.
And we have two new questions that I'm going to be adding to the benchmark.
And we're going to be testing them out today.
So stick around to the end for that.
And we're going to see, is this really the GPD 4 killer?
Let's find out.
So this is the blog post introducing the next generation of Cloud.
So this is Cloud 3.
Now, the previous versions of Cloud have been pretty good.
It is a closed source model. It is paid, but the performance has been good.
And I've heard that it's especially good at creative writing and
they're following the trend of releasing multiple models, which I really like.
They have three versions. They have Haiku, Sonnet and Opus. Each are different sizes and different prices and different speeds.
And so I really like this approach because companies that are releasing multiple models like Mistral,
You get to choose the appropriate model for the given use case.
So let's say you need really fast responses and you don't have complex prompts,
you take the small model because it's fast and cheap.
If you have everyday tasks that aren't really cutting edge,
then you can use their standard model.
And then if you have cutting edge tasks that you need the best of the best,
you pay for the best, but you can get their largest model.
And right here it says each successive model offers
increasingly powerful performance, allowing users to select the optimal balance
of intelligence speed and cost. So again, I really like this approach and I'll tell you a little
bit about when you should use one over the other. So here we go. In very Apple fashion, we have
on the Y-axis, we have intelligence based on benchmarks and then on the X-axis, we have cost
per million tokens. And here's the curve right here. So cloud three, high coup, smallest model,
lowest on the intelligence score and also by far the cheapest. Then we have cloud,
on it in the middle and then opus on the higher end. So how do you choose which model to use?
Well, I think the way to think about it is if you have standard use cases like creative writing,
summarization, other things like that, you could probably use Cloud3Sonic, which is their middle model.
Then if you find that you're getting great responses every single time, I'd move it down to
Hiku and try it there because it's a fraction of the cost and it's going to be a lot faster.
Now, if you have cutting edge needs, whether that's using it for agents or coding or math
or difficult logic, then that's probably when you're going to need Opus.
Now, if I had to think about the breakdown, Haiku and Sonnet will probably cover you in
95% of your use cases.
And then for Opus, you use that for that last 5%.
And a little bit more about it.
This line in particular really stood out to me.
It exhibits near human levels of comprehension and fluency on complex tasks leading the frontier of general intelligence.
And they are making the claim that this is likely AGI. So based on everything we talked about yesterday with the Elon Musk lawsuit against open AI,
it's interesting to see that Clawd3 is actually claiming that it is general intelligence.
And the definition of general intelligence is that AI is as good or better than humans
at the majority of tasks.
Now the cloud models I've heard have always been good at creative writing and they continue
that trend here.
So increased capabilities and analysis and forecasting, nuanced content creation and
that is a very important use case, code generation and conversing in non-English languages.
