Chat
At a glance
- Price per 1M tokens (input)
- $5.00
- Price per 1M tokens (output)
- $30.00
- Context
- 400,000 tokens
- Free access
- yes (see below)
Refreshed daily; data verified October 8, 2026. Published August 24, 2026.
Chat by OpenAI is designed for developers and business teams who need a need for a versatile language model that can interpret images, execute predefined functions through tool calling, and perform multi-step reasoning. It is well suited for tasks such as analyzing visual data in reports, automating workflows by interacting with external systems via APIs, and breaking down complex problems into logical steps. The model distinguishes itself by integrating these capabilities natively, allowing seamless interaction between visual input, functional execution, and reasoned output without requiring external orchestration layers.
Specifications & pricing
| Input (per 1M tokens) | $5.00 |
|---|---|
| Output (per 1M tokens) | $30.00 |
| Cache read (per 1M tokens) | $0.50 |
| Context window | 400,000 tokens |
| Max output | 128,000 tokens |
| Capabilities | images, tool calling, reasoning |
LiteLLM community dataset (MIT), verified October 8, 2026. Official OpenAI pricing.
What Chat would cost on your workload — run it through the cost calculator →
Where to try Chat for free
- OpenAI offers a free chat — ChatGPT (free plan). A vendor's free chat may run a different model from the same family — the exact model is not guaranteed.
Frequently asked questions
What types of tasks benefit most from the image understanding capability?+
Tasks that involve interpreting charts, diagrams, screenshots, or photos — such as extracting data from visual reports, identifying objects in operational images, or assessing document layouts — are well supported by the model’s ability to process and reason about visual inputs alongside text.
How does tool calling work in practice for business applications?+
Tool calling allows the model to invoke specific functions you define, such as querying a database or triggering a workflow in another system, by generating structured calls that your application can execute and return results from, enabling the model to act as an agent in automated processes.
How does this model differ from other versions in the OpenAI line?+
Unlike text-only variants, this model includes native image understanding and is optimized for combining visual perception with function use and logical reasoning, making it more suitable for multimodal agent-like tasks where input may come from both visual and textual sources.
What should users be aware of when using this model for step-by-step reasoning?+
While the model can break down problems into sequential steps, its reasoning depends on the clarity of the prompt and the quality of available information; users should verify critical outputs, especially in high-stakes decisions, and provide explicit instructions when precision is required.
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