DeepSeek: Complete Capabilities Cheatsheet (August 2025)
Best Features to Master
Advanced Reasoning (deepseek-reasoner)
Provides sophisticated problem-solving through reinforcement learning, showing its work via chain-of-thought.
- Emits its reasoning process within <think>...</think> tags, allowing for verification.
- Caveat: The visibility of these reasoning steps depends on the user interface or platform and may sometimes be hidden.
- Excellent at self-verification and correcting its own problem-solving approaches.
Best for: Mathematics, multi-step logical problems, complex coding tasks.
Mixture-of-Experts (MoE) Architecture
Delivers high performance with drastically reduced computational costs by activating only relevant neural sub-networks per query.
- DeepSeek-V2 Model: Features 236 billion total parameters, but only activates 21 billion per token, making it highly efficient.
- Reduces inference costs significantly compared to dense models of similar capability.
Best for: Enterprise deployment, cost-sensitive applications, high-throughput use cases.
Accessible Model Weights
Offers model weights for local deployment and fine-tuning, allowing for full commercial use.
- Licensed under the DeepSeek Model License, which permits commercial use but includes specific usage restrictions (it is not an MIT license).
- Full model weights are available for download and adaptation from platforms like Hugging Face.
Best for: Custom AI development, research, and specialized self-hosted applications.
Model Variants & API Identifiers
DeepSeek offers distinct models for general chat and advanced reasoning, each with specific API identifiers and version tags.
| Model Name | API Identifier | Latest Version Tag (May 2025) | Primary Use Case |
|---|---|---|---|
| DeepSeek Chat | deepseek-chat |
DeepSeek-V3-0324 | General conversation, content creation, summarization, and balanced tasks. |
| DeepSeek Reasoner | deepseek-reasoner |
DeepSeek-R1-0528 | Complex reasoning, mathematics, logic puzzles, and advanced coding problems. |
Open-Weight Models
In addition to the hosted API models, DeepSeek provides several open-weight models (like DeepSeek-V2 and DeepSeek-Coder) on platforms like Hugging Face for community use and self-hosting. These may have different specifications (e.g., larger context windows) than the API versions.
Technical Specifications & Performance
Model Architecture & Specs
- Context Window (API): The hosted API for both `deepseek-chat` and `deepseek-reasoner` supports a 64K token maximum input context.
- Note: The open-weight DeepSeek-V2 model available for self-hosting advertises a 128K token context window. Always check the specs for your specific deployment environment.
- Training Data: Trained on a vast and diverse dataset of trillions of tokens, with a strong emphasis on high-quality code and mathematical texts.
Benchmark Performance (DeepSeek-R1)
The following key benchmark scores were reported in the official DeepSeek-R1 research paper (January 2025):
- AIME-2024 (Math): 79.8%
- MATH-500 (Math): 97.3%
- Codeforces (Competitive Coding): 2029 Elo Rating
- MMLU-Pro (General Knowledge): 84.0%
- GPQA Diamond (Graduate-Level Reasoning): 71.5%
Note on SWE-Bench: While highly capable on coding tasks, independently verified scores for DeepSeek-R1 on SWE-Bench are typically lower than some initial claims, generally falling in the 49-58% range.
Access Options & API Pricing
Web Platform
- Free access is available via the DeepSeek Chat platform for direct interaction.
- Includes a daily message cap for free users (e.g., 50 messages/day), but this is an unofficial limit and subject to change.
API Access
- Pay-per-use pricing model for developers and businesses.
- Offers significant cost savings compared to other leading models.
- Features an intelligent caching system to reduce costs on repeated queries.
Official API Pricing (per 1 Million Tokens)
| Model | Input (Cache Miss) | Input (Cache Hit) | Output |
|---|---|---|---|
| deepseek-chat | $0.27 | $0.07 | $1.10 |
| deepseek-reasoner | $0.55 | $0.14 | $2.19 |
DeepSeek offers a daily off-peak discount window for API usage, providing further cost savings for non-urgent tasks.
Practical Applications
Technical Problem-Solving
Use the `deepseek-reasoner` model to tackle complex technical challenges, from debugging software to solving advanced mathematical problems.
Research & Development
Leverage the 64K context window to feed the model large academic papers or datasets and ask for summaries, insights, or comparative analyses.
Enterprise Solutions
Deploy DeepSeek models for business intelligence, leveraging their ability to analyze structured data and provide insights for supply chain management, financial forecasting, and more.
Pro Tips
Choose the Right Model
Use `deepseek-chat` for general tasks and creative content. Switch to `deepseek-reasoner` specifically for tasks that require deep logic, multi-step planning, or high accuracy in math and code.
Leverage Caching
For applications with repetitive queries (like customer support bots answering common questions), structure your prompts consistently to maximize the benefit of the lower-cost cache hits.
Verify the Reasoning
When using `deepseek-reasoner`, always inspect the output within the `
Strengths & Limitations
Strengths
- Cost-Efficiency: Industry-leading performance at a fraction of the price of competitor models.
- Openness: Availability of model weights under a commercial-use license encourages innovation and custom deployments.
- Advanced Reasoning: The `deepseek-reasoner` model is purpose-built for high-accuracy logical tasks.
- Strong Multilingual Support: Excellent performance in both English and Chinese.
Limitations
- Ecosystem Maturity: The tool and developer ecosystem is less mature compared to established players like OpenAI.
- Creative Tasks: While capable, the models are more optimized for technical and logical tasks than for highly abstract or creative writing.
- API vs. Open-Weight Specs: There can be confusion between the specs of the hosted API (e.g., 64K context) and the more powerful open-weight models (e.g., 128K context).