# OpenAI

Multimodal Integration: Supports text, image, audio (and even video) inputs, processing various data types in a unified manner, suitable for real-time conversations and multimodal interactions.

Low Latency and High Responsiveness: Voice input response time as low as 232 milliseconds (averaging around 320 milliseconds), approaching the immediacy of human conversation.

Cross-language Capability: Supports over 50 languages, performing exceptionally well in non-English scenarios, while employing a new tokenizer to reduce token consumption for non-Latin scripts.

Cost and Efficiency: Faster and less expensive compared to previous versions (such as GPT-4 Turbo), suitable for high-frequency real-time interactive applications.

Lightweight Design: As a smaller, lower-cost version of GPT-4o, it features a reduced size, faster operation speed, and lower API usage costs.

High Cost-Performance Ratio: Costs only about 15 cents per million input tokens and about 60 cents per million output tokens, ideal for large-scale deployment scenarios.

Basic Multimodal Support: Despite its reduced size, it retains basic text and image input capabilities, suitable for most routine tasks.<

Context Window: Still supports large context (such as 128K tokens), suitable for long document analysis and maintaining consistency in complex conversations.

Focus on Deep Reasoning: Designed with emphasis on step-by-step reasoning for complex problems, mathematical and programming tasks, employing a "think first, then answer" strategy to generate logically rigorous responses.

High Accuracy: Demonstrates strong performance in science, engineering, and logical reasoning tasks, suitable for professional domains requiring deep thinking.

Higher Computational Resource Consumption: Due to more complex reasoning processes, response speed and energy consumption are relatively higher, more suitable for scenarios with high accuracy requirements.

Lightweight Version of o1: Streamlined and optimized based on o1, aimed at reducing computational costs and improving response speed.

Balanced Performance and Efficiency: Though somewhat compromised in reasoning depth, it still provides sufficient logical reasoning capabilities for most practical application scenarios.

Suitable for Frequent Calls: Offers a good option for budget-sensitive applications or scenarios requiring high-speed reasoning responses.

Next-Generation Reasoning Model: Further optimizes reasoning capabilities based on the o1 series, aiming to handle more complex multi-step logical problems and task decomposition.

Enhanced Multi-step Reasoning Capability: Suitable for tasks requiring multi-stage analysis, complex mathematics, or programming, expected to bring higher performance to scientific research and industrial applications in the future.

Currently in Testing/Early Deployment Stage: Some o3 models may not yet be widely publicly available, with development focus on further improving accuracy and stability.

Enhanced Conversation and Emotional Intelligence: Compared to GPT-4o, places greater emphasis on the fluency of natural language dialogue and emotional recognition, capable of capturing subtle tone changes in conversations, making responses closer to human communication.

Extensive Knowledge Coverage: Possesses a larger, more comprehensive knowledge base, reducing the rate of hallucinations (inaccurate information), suitable for complex content generation, writing, and creative tasks.

Higher Cost: As one of the most powerful general dialogue models currently available, training and operational costs are relatively high, suitable for scenarios demanding high-quality output with sufficient budget.

Optimized Version: Upgraded based on GPT-4, focusing on improving response speed and reducing usage costs, an ideal choice for real-time applications and large-scale deployment.

Large Context Window: Supports context windows up to 128K tokens, suitable for long document analysis and maintaining consistency in complex conversations.

Economically Efficient: Significantly reduces the cost per million tokens while maintaining high-quality generation, very suitable for cost-sensitive business applications.

Flagship Large Model: Launched in 2023, GPT-4 possesses powerful language understanding and generation capabilities, supporting multi-task and multimodal operations (accepting image input in ChatGPT).

Widespread Application: Performs excellently in various professional examinations, programming, content generation, and other tasks, but slightly inferior to the Turbo version in terms of response speed and cost.

Stability and High Quality: Suitable for scenarios with high requirements for generation accuracy and language quality, but with relatively higher cost and latency per call.

Historical Achievement: As an optimized version of GPT-3.5, known for its low cost and high speed, it is one of the most commonly used models for ChatGPT free and Plus users.

Real-time Conversation Advantage: Responds quickly, suitable for daily chat, simple content generation, and code completion tasks, although its capability in complex reasoning and in-depth analysis is not as strong as the GPT-4 series.

Economically Efficient: Low cost, suitable for large-batch real-time interactions, but may require higher-level model support when handling complex tasks.