Discover AI’s Next Evolution

OpenAI’s O-series models are changing machine reasoning with advanced logical deduction and multi-step planning.

The o4-mini model offers a larger context window, higher accuracy, and better tool support for complex tasks. This allows for more advanced AI applications.

It is a good choice for enterprise use because it provides strong reasoning and decision-making while being cost-effective. This makes it ideal for companies looking to improve their AI capabilities without sacrificing performance.

Understanding OpenAI’s O-Series: The Evolution of AI Reasoning Models

April 25, 2025

The world of AI models is changing quickly. Specialized models designed for specific tasks are becoming more important. In this article, I will explore OpenAI’s O-series models, which are a big step forward in AI reasoning.

What Makes Reasoning Models Different?

Imagine a detective at a complex crime scene. They do not just list the objects they see. They analyze blood spatter, connect footprints to witness statements, figure out motives, and build a logical sequence of events. Standard AI models might be good at describing the scene, but reasoning models act like that detective. They go deeper, make logical deductions, solve complex problems, and plan multi-step actions.

These specialized AI systems are designed for tasks that require logical deduction, inference, planning, and multi-step problem-solving. Unlike standard language models that are good at generating fluent text, reasoning models can understand abstract concepts, identify logical structures, and connect different pieces of information to reach solid conclusions.

This article is based on this chapter in this book if you want more details.

The O-Series Model Lineup

OpenAI’s O-series models are built for logical deduction, multi-step planning, and STEM tasks. They are designed to “spend more internal tokens thinking before speaking.” This leads to higher accuracy on math, code, and complex planning tasks.

The current lineup includes:

Key Improvements in the o4 Generation

The o4 generation is OpenAI’s next step in dedicated reasoning models. The o4-mini model, which is now generally available, fixes many of the problems of the o3-mini:

1. Larger context with no latency penalty: o4-mini has a 4x larger context window (from 32K to 128K tokens) but is still faster than o3-mini at the same reasoning level.
2. Full tool support: Unlike o3-mini, o4-mini can browse the web, run Python, analyze files and images, call functions, and generate images through the standard Chat Completions API.
3. Higher accuracy: Benchmarks show that o4-mini is as good as o3 “medium effort” and much better than o3-mini on math, science, and software engineering tasks.
4. Enhanced reasoning capability: The reasoning_effort parameter still works well. “High” settings lead to deeper chains of thought while keeping costs much lower than o3.

The full o4 model, which is still in a limited developer preview, promises even more features:

• 256K target context window for very long research and reasoning chains.
• Built-in reasoning summaries with the reasoning_summary=detailed parameter.
• Better tool orchestration with more deterministic parallel function calls.

Important Parameters for O-Series Models

When you work with O-series models, you need to understand some important API parameters.

max_completion_tokens replaces max_tokens

• max_tokens is rejected by every O-series model.
• Use max_completion_tokens instead.
• Most client libraries added this field in late 2024. You should upgrade to openai-python 1.14 or newer to avoid errors.

reasoning_effort — the “think-time” dial

reasoning_effort: "low" | "medium" | "high"   # default = "medium"

• High → more hidden reasoning tokens → better accuracy but higher latency and cost.
• Low → fewer reasoning tokens → faster, cheaper replies (good for simple tasks).

Quick-Start Python Example

Here is a simple example of how to use o4-mini with the OpenAI API:

import os, openai
from dotenv import load_dotenv
load_dotenv()
openai.api_key = os.getenv("OPENAI_API_KEY")

response = openai.chat.completions.create(
    model="o4-mini",  # Using the newest generally available model
    messages=[
        {"role": "system",
         "content": "You are a careful math tutor. Think step-by-step."},
        {"role": "user",
         "content": "Prove that the sum of the first n odd numbers is n^2."}
    ],
    reasoning_effort="high",          # give it more "think time"
    max_completion_tokens=512,
    temperature=0.2
)
print(response.choices[0].message.content)

Effective Prompting for Reasoning Models

When you work with reasoning models, some prompting techniques are very useful:

• Ask for chain-of-thought explicitly: “Show your reasoning before the final answer.”
• Provide intermediate scratch-pads: Guide multi-step deduction with sub-questions.
• Use structure tags: <scratchpad>...</scratchpad> tags let you remove internal reasoning later.
• Include few-shot demonstrations: Examples with both reasoning steps and final answers reduce the chances of the model making up logic.

Migration Decision Guide

Are you wondering if you should switch to the newest models? Here is a quick decision chart:

Remaining Limitations to Be Aware Of

Even with its improvements, o4-mini still has some limitations:

• Fine-tuning: Not supported for any O-series model (but OpenAI says it is “on the roadmap”).
• Parameter compatibility: Some libraries still reject reasoning_effort or max_completion_tokens.
• Vision token cost: Image inputs use a lot of tokens. Each 512×512 image tile uses about 5,667 tokens.
• Occasional hallucinations: While it is better than o3-mini, the model still sometimes makes things up in edge cases.

Migration Checklist

If you are planning to use O-series models, here is a migration checklist:

1. Update SDKs (OpenAI Python ≥ 1.14, JS ≥ 4.5).
2. Replace every max_tokens with max_completion_tokens.
3. Decide on a global default for reasoning_effort (start with medium).
4. Add regression tests to measure accuracy versus latency at each effort level.
5. For most production reasoning tasks, use o4-mini-medium for the best balance of price and quality.

OpenAI reasoning models are arguably the best all-around reasoning models as of April 2025. They do have competitors. You can get reasoning models even if you have to target Bedrock or Vertex.

Competitive Landscape: Alternative Reasoning Models

Several AI platforms offer specialized reasoning models that compete with OpenAI’s o3 and o4 models. Here is how they compare across the major platforms:

Google VertexAI

• Gemini Ultra: Google’s top model leads in mathematical reasoning with AlphaCode 2 integration and strong multimodal analysis capabilities. It offers robust tool integration but has enterprise pricing and a steeper learning curve than OpenAI’s offerings.
• Gemini Pro: Offers a balance between performance and cost. It has solid reasoning capabilities for everyday use cases, but it is not as powerful as Gemini Ultra for complex math problems.

Amazon Bedrock

• Claude 3 Opus: Anthropic’s general-purpose model on AWS Bedrock offers state-of-the-art reasoning (MMLU: 86.8%) and a 200K token context. It is good at long-context analysis but has higher latency and a more complex API.
• Claude 3 Sonnet: Balances speed and accuracy. It is preferred in 60-80% of expert evaluations, making it competitive with o4-mini in many use cases.
• Amazon Titan Models: While not specifically focused on reasoning like the O-series, Titan models support various reasoning tasks and are optimized for AWS integration.

Perplexity AI

Perplexity offers several specialized reasoning models for different use cases:

• DeepSeek-R1: An open-source reasoning specialist with 671B mixture-of-experts parameters, a 128K token context, and 32K reasoning tokens. It has a 27x cost advantage over o1 and transparent reasoning logs, but it has less tool integration than OpenAI models.
• sonar-reasoning-pro: Perplexity’s top reasoning offering, powered by DeepSeek R1 with Chain of Thought capabilities. It is designed for complex multi-step tasks.
• sonar-reasoning: A faster real-time reasoning model designed for quick problem-solving with integrated search capabilities.
• sonar-deep-research: An expert-level research model that conducts exhaustive searches and generates comprehensive reports. It is ideal for in-depth analysis across multiple information sources.

Comparative Analysis

Each platform has unique advantages. Google VertexAI is good at mathematical and multimodal reasoning. AWS Bedrock provides enterprise-grade integration with Claude’s reasoning capabilities. Perplexity offers cost-effective specialized research models and integrated search capabilities.

I tend to mix and match models and try different combinations for different tasks.

def init_default_providers(llm_manager):
    # Register available providers
    # gemini-pro, gemini-think, gemini-flash

    try:
        google_provider = GoogleGeminiProvider(model="gemini-2.0-flash")
        llm_manager.register_provider("gemini-flash", google_provider)
        logger.info("Registered Google Flash provider")
    except Exception as e:
        logger.warning(f"Failed to initialize Google Flash provider: {e}")

    try:
        google_provider = GoogleGeminiProvider(model="gemini-2.0-flash")
        llm_manager.register_provider("google-flash", google_provider)
        logger.info("Registered Google Flash provider")
    except Exception as e:
        logger.warning(f"Failed to initialize Google Flash provider: {e}")

    try:
        google_provider = GoogleGeminiProvider(model="gemini-2.5-pro-preview-03-25")
        llm_manager.register_provider("gemini-pro", google_provider)
        logger.info("Registered Google provider")
    except Exception as e:
        logger.warning(f"Failed to initialize Google Pro provider: {e}")

    try:
        google_provider = GoogleGeminiProvider(model="gemini-2.5-pro-preview-03-25")
        llm_manager.register_provider("google-pro", google_provider)
        logger.info("Registered Google provider google-pro")
    except Exception as e:
        logger.warning(f"Failed to initialize Google Pro provider: {e}")

    try:
        google_provider = GoogleGeminiProvider(model="gemini-2.0-flash")
        llm_manager.register_provider("gemini-flash", google_provider)
        logger.info("Registered Google Flash provider")
    except Exception as e:
        logger.warning(f"Failed to initialize Google Flash provider: {e}")

    try:
        google_provider = GoogleGeminiProvider(model="gemini-2.0-flash-thinking-exp-01-21")
        llm_manager.register_provider("gemini-think", google_provider)
        logger.info("Registered Google provider")
    except Exception as e:
        logger.warning(f"Failed to initialize Google Think provider: {e}")

    try:
        google_provider = GoogleGeminiProvider(model="gemini-2.0-flash-thinking-exp-01-21")
        llm_manager.register_provider("google-think", google_provider)
        logger.info("Registered Google provider")
    except Exception as e:
        logger.warning(f"Failed to initialize Google Think provider: {e}")

    try:
        google_provider = GoogleGeminiProvider()
        llm_manager.register_provider("google", google_provider)
        logger.info("Registered Google provider")
    except Exception as e:
        logger.warning(f"Failed to initialize Google provider: {e}")

    # Try to register OpenAI as a fallback provider
    try:
        openai_provider = OpenAIProvider()
        llm_manager.register_provider("openai", openai_provider)
        logger.info("Registered OpenAI provider")
    except Exception as e:
        logger.warning(f"Failed to initialize OpenAI provider: {e}")

    try:
        openai_provider = OpenAIProvider(model="gpt-4o-2024-08-06")
        llm_manager.register_provider("gpt-4o", openai_provider)
        logger.info("Registered OpenAI provider gpt-4o")
    except Exception as e:
        logger.warning(f"Failed to initialize gpt-4o OpenAI provider: {e}")

    try:
        openai_provider = OpenAIProvider(model="o3-mini-2025-01-31")
        llm_manager.register_provider("o3-mini", openai_provider)
        logger.info("Registered OpenAI provider")
    except Exception as e:
        logger.warning(f"Failed to initialize o3-mini OpenAI provider: {e}")

    try:
        openai_provider = OpenAIProvider(model="gpt-4o-mini-2024-07-18")
        llm_manager.register_provider("gpt-4o-mini", openai_provider)
        logger.info("Registered OpenAI provider gpt-4o-mini")
    except Exception as e:
        logger.warning(f"Failed to initialize gpt-4o-mini OpenAI provider: {e}")

    try:
        openai_provider = OpenAIProvider(model="gpt-4o-search-preview-2025-03-11")
        llm_manager.register_provider("gpt-4o-search", openai_provider)
        logger.info("Registered OpenAI provider gpt-4o-search")
    except Exception as e:
        logger.warning(f"Failed to initialize gpt-4o-search OpenAI provider: {e}")

    try:
        perplexity_provider = PerplexityProvider()
        llm_manager.register_provider("perplexity", perplexity_provider)
        logger.info("Registered Perplexity provider")
    except Exception as e:
        logger.warning(f"Failed to initialize Perplexity provider: {e}")

    try:
        perplexity_provider = PerplexityProvider(model="sonar")
        llm_manager.register_provider("sonar", perplexity_provider)
        logger.info("Registered Perplexity provider")
    except Exception as e:
        logger.warning(f"Failed to initialize Perplexity provider: {e}")

    try:
        perplexity_provider = PerplexityProvider(model="sonar-reasoning")
        llm_manager.register_provider("sonar-reasoning", perplexity_provider)
        logger.info("Registered Perplexity provider")
    except Exception as e:
        logger.warning(f"Failed to initialize Perplexity provider: {e}")

    try:
        perplexity_provider = PerplexityProvider(model="sonar-reasoning-pro")
        llm_manager.register_provider("sonar-reasoning-pro", perplexity_provider)
        logger.info("Registered Perplexity provider")
    except Exception as e:
        logger.warning(f"Failed to initialize Perplexity provider: {e}")

    try:
        anthropic_provider = AnthropicProvider()
        llm_manager.register_provider("anthropic", anthropic_provider)
        logger.info("Registered Anthropic provider")
    except Exception as e:
        logger.warning(f"Failed to initialize Anthropic provider: {e}")

If you enjoyed this article, check out this chapter in this book.

Conclusion

The O-series models are a big step forward in AI reasoning. While o3 and o3-mini laid the groundwork, o4-mini now offers the best balance of reasoning capability, tool support, and cost for most applications. The full o4 model, when it becomes generally available, will provide even greater capabilities for the most demanding reasoning tasks.

By understanding the strengths, limitations, and proper use of these models, developers can build more advanced applications that can solve complex problems, plan, and analyze.

Have you tried the O-series models yet? Share your experiences in the comments below.

About the Author

Rick Hightower is a technology expert and AI enthusiast with a lot of experience in enterprise software development and cloud architecture. As a respected voice in the AI community, Rick specializes in evaluating and implementing new AI models for practical business applications. He regularly shares his insights on new AI technologies and their real-world implications through technical articles and conference presentations.

Connect with Rick: LinkedIn