DeepSeek Chat

Yes, DeepSeek Chat provides unified access to both the DeepSeek-V3 and DeepSeek-R1 model engines. DeepSeek-V3 (the deepseek-chat model) is a 671-billion-parameter Mixture-of-Experts model built for rapid, general-purpose text generation and coding. DeepSeek-R1 (the deepseek-reasoner model) is a dedicated reasoning engine trained with large-scale reinforcement learning to solve complex logic, math, and code problems through explicit step-by-step thinking.

Use deepseek-chat for fast, cost-effective conversational tasks, agent loops, translations, and general programming where low latency is required. Use deepseek-reasoner when you need rigorous mathematical proofs, multi-step logical deductions, or deep architectural debugging where correctness matters far more than response speed. The reasoner model generates internal chain-of-thought reasoning before returning its final answer.

The generic endpoint connects directly to native endpoints for the lowest possible latency and direct token streaming, while the OpenRouter endpoint adds dynamic multi-provider routing and fallback resilience for high-concurrency production workloads. Choose the generic variant for direct development with minimal network hops, and choose OpenRouter when your production pipeline requires automated load balancing during peak traffic windows.

For deepseek-chat, use standard system prompts to define personas and formatting; for deepseek-reasoner, omit system prompts entirely and keep temperature strictly between 0.5 and 0.7. Reinforcement-learning reasoning models perform best with zero-shot user instructions, whereas system prompts or excessive temperatures can induce repetitive loops and degrade logical flow.

No, DeepSeek Chat models are strictly text-only and do not natively process image, video, or audio files. To analyze content from documents, spreadsheets, or visual assets, pre-parse and convert the data into structured plain text or Markdown format before submitting it in the prompt field.