DeepSeek Reasoner or DeepSeek Chat? Picking the Right Variant for Your Call
DeepSeek offers two main model variants: the reasoner and the chat model. This article explains how to choose between them based on task complexity, latency needs, and token billing, and when using the reasoner is unnecessary.
Understanding the Two Variants
DeepSeek provides two distinct model variants: a reasoning-focused model (often called DeepSeek Reasoner) and a chat-optimized model (DeepSeek Chat). The reasoner is designed for complex problem-solving, while the chat model excels at conversational and straightforward tasks.
When to Use the Reasoner
Use the reasoner for tasks that require multi-step logic, such as:
- Mathematical proofs or complex calculations
- Debugging intricate code
- Strategic planning with multiple constraints
- Analyzing nuanced arguments
The reasoner spends more compute on internal reasoning, which can lead to higher accuracy on hard problems.
When to Use the Chat Model
Opt for the chat model for:
- Simple Q&A or retrieval-based responses
- Casual conversation
- Text summarization
- Basic code generation
The chat model responds faster and typically uses fewer tokens, making it more cost-effective for straightforward requests.
Latency and Token Billing Differences
Latency: The reasoner may take longer to respond because it generates additional reasoning tokens before producing the final answer. The chat model streams responses more quickly.
Token billing: Both variants bill based on input and output tokens. However, the reasoner's internal reasoning tokens are also counted as output tokens. This means a single reasoner call can consume significantly more tokens than a chat call for the same prompt.
On our platform, you pay the official price multiplied by 1.3 for all models. So if a reasoner call uses more tokens, your cost increases proportionally.
When the Reasoner Is Wasted Spend
Using the reasoner for simple tasks is inefficient. Examples include:
- Asking for the weather
- Requesting a definition
- Generating a basic greeting
In these cases, the reasoner's extra reasoning steps add no value but increase latency and token usage, leading to higher costs without better results.
Practical Decision Framework
- Assess task complexity: If the problem can be solved with a single logical step or pattern matching, use the chat model.
- Consider latency requirements: For real-time interactions, the chat model is preferable.
- Estimate token usage: For long-form reasoning, the reasoner may be worth the extra tokens; otherwise, stick with chat.
- Test and compare: Run both variants on a sample of your tasks and compare quality, latency, and token counts.
Cost Considerations on Our Platform
Our platform charges 1.3 times the official price for all API calls, regardless of variant. If you are a key contributor, you receive credits at 1.1 times the official price (or 1.2 times for premium contributions) in USDC. This means that while the reasoner costs more due to higher token usage, the multiplier remains the same. Choose the variant that best fits your task to avoid unnecessary spending.
Summary
Pick the reasoner only when the task demands deep, multi-step reasoning. For everything else, the chat model delivers faster responses at lower token cost. By matching the variant to the task, you optimize both performance and spend.