Deepseek: Features, Pricing & Accessibility

DeepSeek has emerged as a game-changing force in artificial intelligence, challenging established giants like OpenAI and Google with its innovative approach to AI development.

This Chinese startup, backed by the $8 billion quantitative hedge fund High-Flyer, has achieved remarkable success with its R1 model, which outperforms OpenAI’s O1 on multiple reasoning benchmarks while maintaining significantly lower operational costs¹ .

Key Takeaways:

• DeepSeek R1 outperforms OpenAI’s O1 on reasoning benchmarks
• Trained with only 2,000 GPUs vs competitors’ 16,000+ GPUs
• Fully open-source under MIT license
• Catalyst for China’s AI pricing revolution
• Focus on research over commercialization

Latest Model: DeepSeek V3 & R1

DeepSeek V3-0324 Highlights

Performance Improvements DeepSeek V3-0324 represents a significant leap forward, achieving top rankings on critical benchmarks:

• MMLU-Pro: Advanced reasoning capabilities
• GPQA Diamond: Scientific question answering
• AIME 2024: Mathematical problem solving
• LiveCodeBench: Real-world coding performance

The model demonstrates competitive performance with Claude 3.5 Sonnet across various evaluation metrics.

Technical Specifications

• Model Size: ~641GB (full precision)
• License: MIT (fully open-source)
• Distribution: Available via Hugging Face
• Quantization Options:
  • 2.71-bit: Optimal balance of performance and efficiency
  • 1.78-bit: Maximum compression (with quality trade-offs)

Background and Funding

DeepSeek was founded by Liang Wenfeng, whose previous venture was High-Flyer, a quantitative hedge fund valued at $8 billion and ranked among the top four in China. Unlike many AI startups that rely on external investments, DeepSeek is fully funded by High-Flyer and has no immediate plans for fundraising.

Models and Pricing

• (1) The 

 model has been upgraded to DeepSeek-V3.  points to the new model DeepSeek-R1.

(2) CoT (Chain of Thought) is the reasoning content 

 gives the final answer before output.

(3) If 

 is not specified, the default maximum output length is 4K. Please adjust to support longer outputs.

(4) Please check DeepSeek Context Caching for the details of Context Caching.

(5) The form shows the original price and the discounted price. From now until 2025-02-08 16:00 (UTC), all users can enjoy the discounted prices of API. After that, it will recover to full price. 

(6) The output token count of 

 includes all tokens from CoT and the final answer, and they are priced equally² .

Top 5 Features of Deepseek

1. Hybrid “Think / Non-Think” Inference Modes

One of DeepSeek’s more novel features is its ability to switch between two inference behaviors: a reasoning-oriented “Think” mode (which encourages internal chain-of-thought and multi-step logic) and a faster, more direct “Non-Think” mode (which prioritizes speed and concise responses).

• This allows users or developers to optimize for either speed or depth/accuracy depending on the task.
• The toggle is exposed in the chat UI and can also be controlled via API parameters.
• In V3.1, DeepSeek emphasized gains in “thinking efficiency” and improved multi-step reasoning ability. ³
• The hybrid nature is further described in analysis of DeepSeek-V3.1, which positions it as combining general-purpose and reasoning skills within one model⁴ .

2. Sparse Attention / Long-Context Efficiency (V3.2-Exp)

DeepSeek’s V3.2-Exp release introduced DeepSeek Sparse Attention (DSA), aimed at improving performance and reducing compute cost for very long contexts.

Key points:

• DSA is designed to apply attention sparsely (i.e. not fully dense attention over all pairs), which can reduce memory and computation burdens in long sequences.
• It is marketed as enabling “faster, more efficient training & inference on long context.”
• DeepSeek also announced that, with V3.2-Exp, API pricing was cut by more than 50 % in many cases.

3. Open-source / Model Accessibility (R1 & Distilled Variants)

Unlike many closed LLM providers, DeepSeek has pushed for open model access, especially with its R1 reasoning line.

• The R1 series is released under the MIT license (for the model weights), enabling more permissive use.

4. Mixture-of-Experts / Efficiency Techniques

DeepSeek integrates architectural and training optimizations to reduce cost, improve scalability, and maintain performance. Some of the techniques highlighted or documented include:

• Mixture-of-Experts (MoE) architectures (DeepSeek-MoE) used to activate only subsets of the model per example, lowering compute for many tasks.
• Latent compression of key-value (KV) cache via Multi-head Latent Attention (MLA) — reducing memory footprint while preserving effective context processing ⁵ .
• In public documents, DeepSeek has claimed much lower training and inference cost relative to comparable models (though scrutiny exists). For instance, its R1 model’s reported training cost ($294,000) is orders of magnitude below figures often quoted for frontier models⁶ .

5. Agent / Tool Integration & Function-Calling

DeepSeek supports (or is actively improving) integration with tools, agents, and function calling a capability increasingly crucial for real-world applications.

• In the V3.1 update, DeepSeek highlighted upgrades in “tools & agents” and improved results on benchmarks like SWE / Terminal-Bench (benchmarks that test tool-based tasks).
• Because DeepSeek models are available in open form (R1, distilled, etc.), developers can embed them into agentic systems more freely and build tool wrappers or orchestration around them.

Consideration: Moderation / Content Behavior

While not technically a “feature” in the positive sense, DeepSeek’s content moderation / information suppression behavior has drawn attention in research. For instance:

• An audit study (2025) found evidence that DeepSeek sometimes suppresses politically sensitive content: internally it may reason on material but selectively omit or transforms it in the final output
• In multimodal use cases (e.g. vision + language in DeepSeek Janus), adversarial prompts have induced hallucinations in manipulated visual inputs

Availability of Deepseek

DeepSeek specializes in open-source large language models (LLMs). As of January 2025, it has made its AI models, including the DeepSeek-R1, available through multiple platforms:

• Web Interface: Users can access its AI capabilities directly through their official website. 
• Mobile Applications: Offers free chatbot applications for both iOS and Android devices, providing on-the-go access to their AI models.
• API Access: Developers and businesses can integrate DeepSeek’s AI models into their applications via the provided API platform.

Comparison with GPT

Results of DeepSeek-R1-Lite-Preview Across Benchmarks

DeepSeek-R1-Lite-Preview achieved strong results across benchmarks, particularly in mathematical reasoning. Its performance improves with extended reasoning steps.

Source: DeepSeek⁷

Challenges and Limitations

Technical Challenges

1. Computational Scaling Despite MoE optimization, broader applications still require significant computational power, limiting accessibility for smaller organizations.

2. Inference Latency Chain-of-thought reasoning, while enhancing problem-solving capabilities, can slow response times for real-time applications.

3. Model Deployment The large model size (~641GB) presents significant challenges for local deployment, requiring high-end hardware or cloud platforms.

Market and Integration Challenges

4. Ecosystem Integration Ensuring seamless compatibility with existing AI tools and workflows requires continuous updates and improved documentation.

5. Market Competition Competing against established giants like OpenAI and Google presents significant adoption challenges despite cost advantages.

6. Open-Source Trade-offs While fostering innovation, open-source availability raises concerns about security vulnerabilities, potential misuse, and limited commercial support.

Quality and Bias Concerns

7. Model Transparency Like all AI models, DeepSeek may inherit biases from training data, requiring continuous monitoring and refinement.

FAQ