OpenAI's o3 family was an exciting development in reasoning-focused AI. The appeal behind our original article remains: models designed for difficult multi-step work can be useful for coding, mathematics, analysis and learning. Understanding their actual capabilities is more helpful than treating every answer as guaranteed correct.
Historical context: this article began with the December 2024 preview. The released o3-mini arrived on 31 January 2025, while o3 launched on 16 April 2025. The comparison below describes those named models, not the newest models available today.
OpenAI, ChatGPT and Model Names
OpenAI develops AI models and products. ChatGPT is an application that can make models and tools available through its interface; it is not itself the name of one underlying model. The API is a separate way for developers to integrate supported models into software.
A product's tools, subscription access and interface controls should not be assumed to exist in every API endpoint. Similarly, an API model's documented inputs and outputs do not tell you everything a particular ChatGPT experience can do. Check the relevant product and model documentation together.
Why Reasoning Became a Distinct Capability
Language models can generate useful text from instructions and context. Some tasks also require several interdependent decisions: following a constraint, checking a calculation, finding an exception and assembling the result. An early mistake can affect later steps even when the final response sounds fluent.
Reasoning-focused training and additional inference-time work aim to improve performance on such tasks. That does not mean ordinary language models are incapable of reasoning, nor that a reasoning model is always the fastest or best choice for a short summary. Compare the model's actual output on the job you need it to do.
What o3 Was Designed to Do
The April 2025 o3 launch emphasised reasoning across coding, science, mathematics and visual tasks. o3 accepts text and image inputs and produces text output. Its ability to analyse an image does not mean the base model directly accepts audio or generates pictures as its native output.
Tool use extends what an application can do, but tools must be available and correctly connected. A model can request an action or produce arguments for a function; the surrounding system controls execution, permissions and handling of results. Do not assume a model automatically has live web access or permission to change files simply because a launch demonstration used tools.
What o3-mini Offered
The o3-mini release positioned it as a smaller, cost-efficient reasoning option with particular strength in science, mathematics and coding. Smaller did not mean only basic conversation or a model that readers could run locally on a low-powered computer.
Its documented input modality is text. Image interpretation is therefore an important distinction from o3. It also supported developer features such as function calling and structured outputs. Those features help integrate a model into an application, but valid formatting alone does not establish that an answer is factually correct.
Documented o3 and o3-mini Capabilities
| Capability | o3 | o3-mini |
|---|---|---|
| Principal positioning | Reasoning across text, code and visual inputs | Smaller reasoning option, especially for STEM work |
| Native input | Text and images | Text |
| Native output | Text | Text |
| Documented context window | 200,000 tokens | 200,000 tokens |
| Documented maximum output | 100,000 tokens | 100,000 tokens |
| API fine-tuning | Not supported | Not supported |
These are published limits for the specific models, not a promise that every request should use the maximum. Tokens are units of model input and output, not interchangeable with words. Internal reasoning can consume part of the available budget, and a long context window does not ensure the model notices every detail in a large input.
See the o3 model reference and o3-mini reference. Do not infer fine-tuning support from a generic API navigation entry: the model-specific support table explicitly marks it unsupported.
Reasoning Effort and Private Reasoning
Reasoning models use internal reasoning tokens to work on a response. Increasing supported reasoning effort can change the balance between work performed, latency and cost. The o3-mini release documented low, medium and high effort settings; those settings are model/API capabilities, not a guarantee that every interface exposes the same menu.
The internal process is not a transcript readers receive in full. A visible explanation or supported reasoning summary is different from raw private chain-of-thought. Ask for useful evidence, assumptions, calculations and checks in the answer rather than assuming an instruction to reveal every internal step will improve reliability.
More effort can help on a difficult task, but it can also take longer and still produce a mistaken answer. Test the setting on representative work instead of assuming the highest option is required for every message.
Context Is Not Automatic Learning
Providing relevant context lets a model use that information during a task. It does not mean the model updates its trained weights or permanently learns from every conversation. Product memory, retrieved documents, conversation state and model training are different mechanisms.
For domain-specific work, you can supply instructions and approved reference material, or build an application that retrieves it. That is useful customisation without claiming unsupported o3 fine-tuning. A retrieval system also needs testing: missing, outdated or contradictory documents can lead to a poor answer even with a capable model.
Practical Uses with Acceptance Checks
- Coding: explain an error, compare a proposed fix and supply tests. Run those tests in an appropriate environment before accepting the change.
- Learning: request an explanation at a defined level, then check examples and calculations against trusted material.
- Analysis: supply the actual data and ask the model to state assumptions and uncertainty. Confirm the result independently.
- Support: provide the real policy and require the response to distinguish policy text from inference; involve a person when the decision needs one.
These remain useful possibilities, rather than claims that o3 is the right current model for every deployment. A small representative evaluation is more informative than an unrelated benchmark score or an enthusiastic headline.
Prompt Clearly and Verify the Result
State the goal, relevant context, constraints and desired output. Separate source material from instructions, include the necessary edge cases and define how success will be judged. Start with a direct prompt; refine it after examining the actual failure rather than adding elaborate wording automatically.
For example, ask a coding assistant to identify the cause of a supplied error, propose the smallest appropriate change and explain how to verify it. Supply only the files and diagnostic material it needs, with credentials removed. Ask it to label unresolved assumptions instead of inventing missing configuration.
OpenAI's reasoning guidance recommends simple instructions and says explicit chain-of-thought prompting is unnecessary. Iteration and clear acceptance criteria still matter.
Current Availability and Migration
The October 2026 documentation check shows both models marked deprecated. OpenAI's schedule lists o3-mini and its January 2025 snapshot for API shutdown on 23 October 2026, and the o3 April 2025 snapshot for shutdown on 11 December 2026. The o3 launch page also says it is no longer available in ChatGPT.
Deprecated and already shut down are different states. For existing software, consult the current deprecation schedule, identify the exact model ID and test the recommended replacement before the relevant deadline. Recheck endpoint features, output handling, latency and costs rather than only swapping a name.
Conclusion
o3 and o3-mini illustrated the value of reasoning-focused models, and our positive view of that direction remains. Their practical contribution is clearer when the historical release, input capabilities, verification requirements and current retirement plans are understood. Use the same care when evaluating newer models: match the tool to the task and verify the work that matters.
