DeepSeek Reasoner
DeepSeek
At a glance
- Price per 1M tokens (input)
- $0.28
- Price per 1M tokens (output)
- $0.42
- Context
- 131,072 tokens
- Free access
- yes (see below)
Refreshed daily; data verified August 15, 2026. Published August 12, 2026.
The DeepSeek Reasoner model is designed for businesses and organizations that require advanced reasoning capabilities to solve complex problems. This model is well-suited for tasks that involve step-by-step reasoning, where a series of logical deductions are needed to arrive at a conclusion. The DeepSeek approach focuses on providing a transparent and explainable reasoning process, which sets it apart from other models. By using this model, users can gain a deeper understanding of the decision-making process and identify potential biases or errors. The model's step-by-step reasoning capability makes it a valuable tool for applications where accountability and transparency are essential.
Specifications & pricing
| Input (per 1M tokens) | $0.28 |
|---|---|
| Output (per 1M tokens) | $0.42 |
| Cache read (per 1M tokens) | $0.028 |
| Context window | 131,072 tokens |
| Max output | 65,536 tokens |
| Capabilities | reasoning |
LiteLLM community dataset (MIT), verified August 15, 2026. Official DeepSeek pricing.
What DeepSeek Reasoner would cost on your workload — run it through the cost calculator →
Where to try DeepSeek Reasoner for free
- DeepSeek offers a free chat — DeepSeek Chat. A vendor's free chat may run a different model from the same family — the exact model is not guaranteed.
Frequently asked questions
What is the DeepSeek Reasoner model good for+
The DeepSeek Reasoner model is good for tasks that require logical and methodical reasoning, such as diagnosing complex problems or evaluating evidence to support a decision
How do I get started with the DeepSeek Reasoner model+
To get started with the DeepSeek Reasoner model, users can begin by defining the problem they want to solve and then providing the necessary input data, after which the model will generate a series of logical steps to arrive at a conclusion
How does the DeepSeek Reasoner model differ from other models in the line+
The DeepSeek Reasoner model differs from other models in the line in its focus on step-by-step reasoning and transparent decision-making processes, which makes it particularly useful for applications where accountability is crucial
What are the limitations of the DeepSeek Reasoner model+
The limitations of the DeepSeek Reasoner model include its reliance on high-quality input data and its potential vulnerability to biases in the data or the reasoning process, which users should be aware of when using the model
Compare with others
Org chart: how to move your company onto AI
A practical map: which company roles and processes AI agents can take over, where to start, and in what order to roll it out.