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A base model is a pretrained starting point, a chat model is designed or adapted to follow instructions in conversational exchanges, and a reasoning model is intended for tasks that benefit from additional multistep processing. These labels describe overlapping aspects of models, not three mutually exclusive types. Choose by the work you need done, then compare quality, speed, and usage cost.
What is a base model?
A base model is the pretrained model before further tuning for instruction-following or conversation. A common training objective is predicting the next token in a sequence. That teaches a model patterns in its training data, but does not by itself ensure that it will reliably interpret and carry out a particular user request.
Post-training can shape a model to respond more usefully to instructions. OpenAI’s InstructGPT paper describes training with human demonstrations and feedback. The precise training recipe varies; “base model” does not mean every provider uses the same process or makes its pretrained checkpoint available to users.
What is a chat model?
A chat model is intended for conversational interaction and instruction-following. In a message-based format, a conversation consists of role-labeled messages, and the model is designed to produce the assistant’s part of the exchange. OpenAI describes this format in its API key concepts and Model Spec.
#1 Best Overall
“Chat” can also refer to the application or interface, not just the model behind it. A chat window may provide access to an instruction-following model, a reasoning-capable model, or a choice of modes. The interface alone does not tell you how the underlying model was trained.
What instruction tuning can change
In OpenAI researchers’ 2022 human evaluation, participants preferred outputs from a 1.3-billion-parameter InstructGPT model over those from a 175-billion-parameter GPT-3 model on the study’s API prompt distribution. This is a result for that evaluation and prompt distribution, not evidence that smaller models generally outperform larger ones.
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What is a reasoning model?
A reasoning model is intended for tasks that benefit from additional multistep processing before it responds. OpenAI’s reasoning models guide describes these models as using internal reasoning tokens and highlights complex problem solving, coding, scientific reasoning, and multistep agent workflows as useful applications.
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Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →That extra processing can have trade-offs: OpenAI notes that higher reasoning effort can increase latency and token use. Some interfaces expose an effort setting; others may not. These descriptions reflect OpenAI’s terminology and guidance, and other providers may use different labels or combine reasoning capabilities with other model features.
How do the three labels relate?
| Label | What it describes | Typical reader implication |
|---|---|---|
| Base | A pretrained starting point, before further instruction or conversation tuning. | Next-token training alone does not guarantee reliable instruction-following. |
| Chat | A model or product experience oriented around conversational turns and user instructions. | Check whether “chat” describes the model, the interface, or both. |
| Reasoning | A model or mode intended to spend additional processing on multistep tasks. | Consider it for demanding work, accounting for possible extra latency and token use. |
These labels are not a universal, exclusive taxonomy. A model can be presented through a chat interface and also offer reasoning capabilities; “base” describes a model’s place in development, while “chat” and “reasoning” describe interaction or intended behavior.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When should you use a chat or reasoning model?
Start with a chat model for routine requests
For ordinary conversation, drafting, summarizing, and routine generation, begin with an instruction-following chat model. These tasks often do not require a separate reasoning mode, though results vary by model and by the request.
Consider a reasoning model for demanding multistep work
Try a reasoning-capable model for challenging analysis, complex coding, scientific problems, or workflows that require several linked steps or tool use. The extra processing may help with suitable tasks, but can also add wait time and token consumption.
Compare on your own representative tasks
Provider recommendations are useful starting points, not independent comparative benchmarks. Test candidate models on the same representative prompts and assess:
Best Value
- Quality and reliability: Does the answer meet the task’s requirements, and does it do so consistently?
- Latency: How long does the complete response take?
- Usage cost: What does the task consume under the provider’s current pricing and usage rules?
- Workflow support: Can the model use the tools or integrations your task needs?
- Available controls: Does the interface expose reasoning effort or other settings relevant to the task?
OpenAI’s reasoning best practices explain that reasoning and non-reasoning model families behave differently and that neither is simply better for every task. Match the model and prompting approach to the work, rather than assuming one category always wins.
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