OpenAI LLM Model

A OpenAI LLM Model is a large neural autoregressive language model that is an OpenAI Model.

• Context:
  • It can typically process Input Content with OpenAI LLM context windows ranging from thousands to millions of tokens.
  • It can typically generate OpenAI LLM output text with OpenAI LLM reasoning capabilities that mimic human-like understanding.
  • It can typically perform OpenAI LLM natural language tasks with OpenAI LLM comprehension capabilities across multiple languages and domains.
  • It can typically handle OpenAI LLM multimodal inputs including OpenAI LLM text processing, OpenAI LLM image understanding, and OpenAI LLM audio interpretation.
  • It can typically optimize OpenAI LLM intelligence per dollar with OpenAI LLM pricing strategies for different market segments.
  • ...
  • It can often serve as an OpenAI LLM foundation for OpenAI LLM fine-tuning and OpenAI LLM specialization.
  • It can often exhibit OpenAI LLM emergent behaviors through OpenAI LLM scale-dependent capabilitys.
  • It can often improve through OpenAI LLM iterative development with OpenAI LLM enhancement cycles.
  • It can often offer OpenAI LLM cached input options for OpenAI LLM cost reduction in repetitive processing scenarios.
  • ...
  • It can range from being a OpenAI Base LLM Model to being a OpenAI Chat LLM Model, depending on its OpenAI training objective.
  • It can range from being a OpenAI General LLM Model to being a OpenAI Task LLM Model, depending on its OpenAI specialization level.
  • It can range from being a Small-Scale OpenAI LLM Model to being a Large-Scale OpenAI LLM Model, depending on its OpenAI parameter count.
  • It can range from being a Narrow-Context OpenAI LLM Model to being a Wide-Context OpenAI LLM Model, depending on its OpenAI context window size.
  • It can range from being a Unimodal OpenAI LLM Model to being a Multimodal OpenAI LLM Model, depending on its OpenAI input processing capability.
  • It can range from being a API-Only OpenAI LLM Model to being a Product-Integrated OpenAI LLM Model, depending on its OpenAI distribution strategy.
  • ...
  • It can integrate with OpenAI LLM Infrastructure for OpenAI computational resources and OpenAI deployment management.
  • It can connect to OpenAI LLM Interfaces for OpenAI user interaction and OpenAI service delivery.
  • It can support OpenAI LLM API Access through OpenAI developer tokens and OpenAI request protocols.
  • It can maintain OpenAI LLM Version Control through OpenAI model dating and OpenAI deprecation schedules.
  • It can operate within OpenAI LLM Resource Constraints including OpenAI token limits and OpenAI computational budgets.
  • It can follow OpenAI LLM Pricing Structures with OpenAI input token costs, OpenAI output token costs, and OpenAI cached input costs.
  • ...
• Examples:
  • OpenAI Flagship LLM Models, such as:
    • OpenAI o1 LLM Models, such as:
      • OpenAI o1-2024-12-17, during advanced reasoning enhancement with 200k input tokens.
      • OpenAI o1-mini LLM Model, during efficiency optimization with 128k context.
    • OpenAI GPT-4o LLM Models, such as:
      • OpenAI GPT-4o LLM Model, during multimodal capability with vision processing.
      • OpenAI GPT-4o-mini LLM Model, during performance optimization with reduced latency.
      • OpenAI GPT-4o-Realtime LLM Model, during speech specialization with real-time processing.
    • OpenAI GPT-4.1 LLM Models, such as:
      • OpenAI GPT-4.1 LLM Model, during million-token context implementation with enhanced coding capability.
      • OpenAI GPT-4.1 Mini LLM Model, during cost optimization with efficient performance benchmarks.
      • OpenAI GPT-4.1 Nano LLM Model, during extreme efficiency implementation with minimal resource utilization.
  • OpenAI Previous Generation LLM Models, such as:
    • OpenAI GPT-4 LLM Models, such as:
      • OpenAI GPT-4 Turbo LLM Model, during capability enhancement with 2024-04 update.
      • OpenAI GPT-4 LLM Model, during base model release with 8k context.
    • OpenAI GPT-3.5 LLM Models, such as:
      • OpenAI GPT-3.5-turbo LLM Model, during chat optimization with improved efficiency.
    • OpenAI GPT-3 LLM Models, such as:
      • OpenAI davinci LLM Model, during early capability development with text completion focus.
      • OpenAI curie LLM Model, during balanced implementation with moderate performance.
  • OpenAI Specialized LLM Models, such as:
    • OpenAI Audio LLM Models, such as:
      • OpenAI Whisper LLM Model, during audio transcription task.
      • OpenAI Text-to-Speech LLM Model, during voice synthesis task.
    • OpenAI Visual LLM Models, such as:
      • OpenAI DALL-E LLM Model, during image generation task.
      • OpenAI CLIP LLM Model, during visual processing task.
    • OpenAI Code LLM Models, such as:
      • OpenAI Codex LLM Model, during code generation and programming assistance.
      • OpenAI Code Interpreter LLM Model, during interactive code execution with data analysis.
  • OpenAI LLM Model Deployments, such as:
    • OpenAI API LLM Models, such as:
      • OpenAI Completions API LLM Model, for text completion service.
      • OpenAI Chat API LLM Model, for conversational interaction service.
    • OpenAI Product LLM Models, such as:
      • OpenAI ChatGPT LLM Model, for consumer conversation application.
      • OpenAI GitHub Copilot LLM Model, for developer assistance tool.
  • OpenAI LLM Model Pricing Groups, such as:
    • OpenAI Premium LLM Models, such as:
      • OpenAI o1-preview LLM Model, with $15 per million input tokens and $60 per million output tokens.
      • OpenAI GPT-4.1 LLM Model, with $2 per million input tokens and $8 per million output tokens.
    • OpenAI Mid-tier LLM Models, such as:
      • OpenAI o1-mini LLM Model, with $3 per million input tokens and $12 per million output tokens.
      • OpenAI GPT-4.1 Mini LLM Model, with $0.40 per million input tokens and $1.60 per million output tokens.
    • OpenAI Economy LLM Models, such as:
      • OpenAI GPT-4o-mini LLM Model, with $0.15 per million input tokens and $0.60 per million output tokens.
      • OpenAI GPT-4.1 Nano LLM Model, with $0.10 per million input tokens and $0.40 per million output tokens.
  • ...
• Counter-Example(s):
  • Anthropic LLM, which uses a constitutional AI approach instead of OpenAI LLM training methodology.
  • HuggingFace LLM, which emphasizes open-source distribution rather than OpenAI LLM commercial service.
  • Google LLM, which follows Google-specific development principles distinct from OpenAI LLM design philosophy.
  • Meta LLM, which incorporates Meta-specific social knowledge different from OpenAI LLM training corpus.
  • Open Source LLM, which enables community modification capability not available in OpenAI LLM model.
• See: Decoder-only Transformer-based LLM, Neural Language Model, Commercial AI System, LLM Evolution, AI Pricing Strategy.

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References

2024-09-20

• LLM

Comparison of OpenAI Models

Model	Input Tokens Cost	Output Tokens Cost	Context Window	Output Limit	Focus
o1-preview	$15 per million (3x)	$60 per million (4x)	128k	32k (7.8x)	Advanced reasoning, complex problem-solving
o1-mini	$3 per million (0.6x)	$12 per million (0.8x)	128k	64k (15.6x)	Speed and efficiency, focused reasoning
GPT-4o	$5 per million (1x)	$15 per million (1x)	128k	4,096 (1x)	Multimodal reasoning (text, vision, audio)
GPT-4o mini	$0.15 per million (0.03x)	$0.60 per million (0.04x)	128k	16,384 (4x)	Cost-efficient, fast reasoning, coding

2023

• Python

model_lst = openai.Model.list()
for model in filtered_model_data:
   print('{:25s} {:15s} {:20s}'.format(model['id'], datetime.fromtimestamp(model['created']).strftime("%Y-%m-%d"), model['owned_by']))

Model ID                  Created Date    Model Name          
ada                       2022-04-07      openai              
babbage                   2022-04-07      openai              
davinci                   2022-04-07      openai              
curie                     2022-04-07      openai              
curie-instruct-beta       2022-04-07      openai              
davinci-instruct-beta     2022-04-07      openai              
text-davinci-001          2022-04-07      openai              
text-ada-001              2022-04-07      openai              
text-babbage-001          2022-04-07      openai              
text-curie-001            2022-04-07      openai              
text-davinci-edit-001     2022-04-13      openai              
code-davinci-edit-001     2022-04-13      openai              
text-davinci-002          2022-04-13      openai              
gpt-3.5-turbo             2023-02-28      openai              
gpt-3.5-turbo-0301        2023-03-01      openai              
gpt-3.5-turbo-16k-0613    2023-05-30      openai              
gpt-3.5-turbo-0613        2023-06-12      openai              
gpt-4-0613                2023-06-12      openai              
gpt-4-0314                2023-06-27      openai              
gpt-4                     2023-06-27      openai

2023

• https://platform.openai.com/docs/models/
  • QUOTE: The OpenAI API is powered by a diverse set of models with different capabilities and price points. You can also make limited customizations to our original base models for your specific use case with fine-tuning.

Latest model	Description	Max tokens	Training data
gpt-4	More capable than any GPT-3.5 model, able to do more complex tasks, and optimized for chat. Will be updated with our latest model iteration.	8,192 tokens	Up to Sep 2021
gpt-4-0314	Snapshot of gpt-4 from March 14th 2023. Unlike gpt-4, this model will not receive updates, and will only be supported for a three month period ending on June 14th 2023.	8,192 tokens	Up to Sep 2021
gpt-4-32k	Same capabilities as the base gpt-4 mode but with 4x the context length. Will be updated with our latest model iteration.	32,768 tokens	Up to Sep 2021
gpt-4-32k-0314	Snapshot of gpt-4-32 from March 14th 2023. Unlike gpt-4-32k, this model will not receive updates, and will only be supported for a three month period ending on June 14th 2023.	32,768 tokens	Up to Sep 2021

2023

• Pascale Fung. (2023). “ChatGPT: What It Can and Cannot Do?." Presentation
  • .

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