Background and Market Impact

DeepSeek is a Chinese AI company that triggered outsized attention after the release of DeepSeek R1 on January 27, 2025. The launch quickly climbed to the top of global download charts and coincided with a broad selloff in multiple tech names (including Nvidia and several major Japanese semiconductor-equipment and electronics companies). The narrative centered on lower development cost, fewer chips used, and architecture-level optimizations aimed at reducing memory pressure and improving inter-chip communication, including a Mixture-of-Experts approach.

Tool Profile

DeepSeek is positioned as a pragmatic alternative to top-tier closed models: a combination of very low API pricing, usable web chat and apps, and a dedicated reasoning lineup (R1/R1.5). It is especially attractive when budget is a primary constraint and you still need reasoning performance for math, coding, structured writing, and analytical tasks.

Comparative Scoring by Key Criteria

What DeepSeek R1 Is Used For

DeepSeek R1 positions itself as a competitor to ChatGPT-class models with a broad set of use cases: solving math problems, learning programming, producing structured long-form text, and drafting research-style materials. The practical differentiator is the combination of reasoning performance with aggressive pricing and accessible deployment options.

Key Advantages

Architecture

DeepSeek R1 uses a Mixture-of-Experts (MoE) architecture: a set of specialized sub-networks where only a subset is activated per request. This design targets higher throughput and better compute efficiency compared to dense models of similar scale.

Open Source

DeepSeek R1 is positioned with an open-source approach compared to many competing proprietary models, enabling teams to inspect, adapt, and extend the model stack for internal needs (subject to the specific license terms used for each release).

Training Approach

Training emphasizes Reinforcement Learning (RL), where the model iteratively improves outputs based on feedback signals, reinforcing behaviors that lead to stronger results and correcting mistakes through repeated optimization cycles.

Key Features

• Web chat and apps, including file uploads
• R1.5 reasoning models and V4 with improved capabilities
• Basic multimodality (varies by model and client)
• Fast streaming output for long responses
• Very low API pricing for high-volume usage

Models

• DeepSeek V4
• DeepSeek R1.5 (reasoning)
• DeepSeek V3.5-Exp

Context Window

• ~128K tokens (V4)
• ~100K tokens (R1.5)

Pricing

• Web chat: free within plan limits (example: 100 chats — $0)
• API: after pricing cuts reported on <
  Submitted by Chris Borden