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6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

How to Access OpenAI o1?

Pankaj9786
Pankaj9786 13 Sep, 2024
5 min read

Introduction

Strawberry is out in the market!!! I hope this will be as fruitful as the recent advancements in artificial intelligence brought by other OpenAI’s latest models.

We have been waiting for GPT-5 for so long, and now OpenAI has released its fact-checking and high reasoning model—OpenAI o1, with a code name of Strawberry.

This marks a significant evolution in AI capabilities, particularly in reasoning and problem-solving tasks, setting a new benchmark in the field. This article will tell you – How to Access OpenAI o1, including its preview and mini versions. 

Here is the comprehensive article on its benchmark, per day limit and more: o1: OpenAI’s New Model That ‘Thinks’ Before Answering Tough Problems

OpenAI o1

Table of contents

Overview of the OpenAI o1 Model Series

The introduction of this model marks a pivotal moment in the evolution of language models. Unlike its predecessors, this model is equipped with advanced reasoning skills to tackle complex queries more accurately. Users can now access OpenAI o1 Preview and OpenAI o1-mini, each offering different levels of functionality and reasoning capabilities.

The Preview model, in particular, is touted as the most advanced, allowing for deeper analysis and problem-solving. The models feature a 128k context window, allowing them to process extensive information in a single query. The o1-preview is priced significantly higher than its predecessor, GPT-4o, while the o1-mini is positioned as a more affordable alternative, approximately 80% cheaper than the o1-preview.

Also read: Evaluating GPT-4o mini: How OpenAI’s Latest Model Stacks Up?

How to Access OpenAI o1/Strawberry?

How to Access OpenAI o1/Strawberry?

You can try the new model on ChatGPT Plus and via OpenAI API. Here are the steps to try the model:

1. For ChatGPT Users

OpenAI o1 for users
  • ChatGPT Plus and Team Users:
    • You can start using the “o1” models in ChatGPT immediately.
    • Model Options: There are two models available: o1-preview and o1-mini.
    • How to Select the Models:
      • Go to the model picker in ChatGPT.
      • Manually select either o1-preview or o1-mini.
    • Message Limits:
      • o1-preview: 30 messages per week.
      • o1-mini: 50 messages per week.
    • OpenAI is planning to increase these message limits and also allow ChatGPT to automatically choose the most appropriate model for a given prompt.
  • ChatGPT Enterprise and Edu Users:
    • Access to both models (o1-preview and o1-mini) will start next week.

2. For Developers

OpenAI o1 for developers
  • API Access:
    • Developers who qualify for API usage tier 5 can start using the “o1” models via the API.
    • Rate Limit: 20 requests per minute (RPM) initially, with plans to increase these limits following additional testing.
    • Current Limitations:
      • The API for “o1” models currently does not support:
        • Function calling
        • Streaming responses
        • System messages
        • Other advanced features
    • Getting Started:
      • Review the API documentation on OpenAI’s website to learn more about integrating and using these models.

3. Additional Information

  • OpenAI is working to enhance access by increasing message limits and adding features like automatic model selection in ChatGPT.
  • The details about this access might evolve, so it’s a good idea to check the official OpenAI resources for the most recent updates.

I hope you are clear on – How to Access OpenAI o1. If you need to start using these models, ensure you have the appropriate subscription or developer tier, and follow the steps provided in your respective ChatGPT interface or the API documentation.

OpenAI o1 Pricing Model

Here’s the pricing model of both o1-preview and o1-mini:

o1-preview

OpenAI o1 preview Pricing Model

A new reasoning model designed for complex tasks that require broad general knowledge.

  • Features:
    • Context Window: Supports up to 128K tokens.
    • Knowledge Cutoff: October 2023.
  • Pricing:
    • o1-preview:
      • Input Tokens: $15.00 per 1 million tokens.
      • Output Tokens: $60.00 per 1 million tokens.
    • o1-preview-2024-09-12:
      • Input Tokens: $15.00 per 1 million tokens.
      • Output Tokens: $60.00 per 1 million tokens.

o1-mini

OpenAI o1 MIni Pricing Model

A fast, cost-efficient reasoning model tailored for coding, math, and science use cases.

  • Features:
    • Context Window: Supports up to 128K tokens.
    • Knowledge Cutoff: October 2023.
  • Pricing:
    • o1-mini:
      • Input Tokens: $3.00 per 1 million tokens.
      • Output Tokens: $12.00 per 1 million tokens.
    • o1-mini-2024-09-12:
      • Input Tokens: $3.00 per 1 million tokens.
      • Output Tokens: $12.00 per 1 million tokens.

Both models are designed to handle large context sizes and offer up-to-date knowledge as of October 2023. The o1-preview model is optimized for complex reasoning tasks across a broad range of topics, while the o1-mini model is focused on delivering fast and cost-effective solutions for coding, mathematics, and scientific inquiries.

Conclusion

OpenAI’s introduction of the o1 series represents a pivotal step toward achieving more human-like reasoning in artificial intelligence. The ability to process information more deeply and reflectively could lead to significant advancements in various fields, including medicine, engineering, and beyond. As OpenAI continues to refine these models, future iterations may enhance their reasoning capabilities further, potentially enabling them to “think” for extended periods, which could revolutionize how AI systems operate.

Want to build a Generative AI model just like ChatGPT, explore this course: GenAI Pinnacle Program

Frequently Asked Questions

Q1. What is the Strawberry model?

Ans. The Strawberry model, officially named o1, is a new series of AI models developed by OpenAI that focuses on enhanced reasoning capabilities. It is designed to tackle complex problems in areas such as mathematics, coding, and scientific reasoning, outperforming previous models like GPT-4o in these tasks.

Q2. How does the o1 model differ from previous models?

Ans. The o1 model employs a “chain-of-thought” reasoning approach, allowing it to break down complex problems into smaller, manageable steps. This method enables the model to spend more time processing queries before generating responses, mimicking human-like reasoning patterns.

Q3. Who can access the o1 model?

Ans. As of September 12, 2024, the o1 model is available to ChatGPT Plus and Team users. Enterprise and Education users will gain access shortly thereafter. Additionally, OpenAI plans to make the o1-mini model available to all ChatGPT Free users in the future.

Q4. What are the costs associated with using the o1 model?

For API users, the o1-preview model costs $15 per million input tokens and $60 per million output tokens. In comparison, GPT-4o is priced at $5 per million input tokens and $15 per million output tokens. The o1-mini model is 80% cheaper than the o1-preview, making it a cost-effective option for specific applications.

AIArtificial IntelligenceHow to Access OpenAI o1?machine learningOpen o1-miniOpenAIOpenAI o1OpenAI o1-previewpython
Pankaj9786
Pankaj9786 13 Sep, 2024

Hi, I am Pankaj Singh Negi - Senior Content Editor | Passionate about storytelling and crafting compelling narratives that transform ideas into impactful content. I love reading about technology revolutionizing our lifestyle.

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