Deepseek
10
About
DeepSeek is a versatile AI model family that combines strong natural language comprehension, multimodal understanding, advanced reasoning, and practical coding assistance. It helps users draft, edit, and summarize text, analyze images alongside text, debug and generate code, and solve multi-step problems with transparent, checkable reasoning. DeepSeek is designed for real-world productivity: teams can use it to summarize research, generate marketing copy, automate data insights, prepare project notes, and accelerate software development.
What makes DeepSeek special is its balance of performance and efficiency. It delivers large-model capabilities while keeping compute and cost manageable, so you can deploy it at scale or run models locally for secure, customized workflows. The model exposes its step-by-step reasoning in visible tags (e.g., ... ), enabling users to follow, verify, and correct the model’s logic when tackling complex math, logic puzzles, or multi-stage coding tasks. Newer releases switch seamlessly between concise direct answers and detailed chain-of-thought modes depending on your needs.
DeepSeek also provides multimodal variants that understand images with text, plus a developer-focused Coder edition that generates, explains, and debugs code across languages and frameworks. Flexible API access, a mobile app, and options for local fine-tuning make it practical for enterprises, researchers, and individual developers. Note that while the family is open-source and fine-tunable, the DeepSeek Model License has specific commercial restrictions to review. Overall, DeepSeek is ideal for users who need accurate, context-aware assistance in writing, research, data analysis, project work, and software development, with transparent reasoning and cost-effective performance.
Percs
Multi-modal
Cost effective
High accuracy
Fast reasoning
Settings
Diversity control- Top_p. Filters AI responses based on probability.
Lower values = top few likely responses,
Higher values = larger pool of options.
Lower values = top few likely responses,
Higher values = larger pool of options.
Response length- The maximum number of tokens to generate in the output.
Temperature- The temperature of the model. Higher values make the model more creative and lower values make it more focused.
Context length- The maximum number of tokens to use as input for a model.
Reasoning- Ability to think deeper