Last updated: 2026-07-01
DeepSeek is a Chinese AI lab's family of open-weight, API-accessible large language models built on a Mixture-of-Experts design; its newest line, V4, ships as two general-availability models, V4-Pro (1.6 trillion parameters) and V4-Flash (284 billion), both aimed at coding, math, and multi-step agentic reasoning tasks.
About DeepSeek
DeepSeek is a Chinese AI company founded in 2023, backed by the quantitative hedge fund High-Flyer rather than outside venture capital. It builds large language models on a Mixture-of-Experts architecture, aiming to match GPT-4-class and Claude-class performance while training at a fraction of the typical budget for frontier labs. Its current lineup runs from the reasoning-focused R1 model up through the newer V4 family, which reached general availability in July 2026 as two models: V4-Pro and V4-Flash. DeepSeek also ships DeepSeek Coder for programming tasks and DeepSeek VL for multimodal input, all available through a free web and mobile chat app or a metered API, with most model weights released under the MIT license for self-hosting and fine-tuning.
Pricing
Free tier: up to 1M input tokens/month + limited output. API pricing: DeepSeek-V3.2 at $0.28/$0.42 per 1M tokens (input/output); DeepSeek-R1 at $0.55/$2.19 per 1M tokens. Cache hit discounts (90% reduction) and off-peak pricing available. Enterprise plans available with custom pricing starting ~$18,000/year for private deployment. V4 API pricing now uses peak/off-peak hours, with peak-hour rates double the off-peak rate; the legacy deepseek-chat and deepseek-reasoner model IDs were retired July 24, 2026.
Key Features
- Advanced Mixture-of-Experts Architecture: 671B total parameters with 37B activated per token using the DeepSeekMoE framework, which keeps inference and training costs down while matching flagship-class benchmark performance from much larger dense models.
- Extended Context Windows: Supports 128K-164K token context windows enabling processing of full documents, codebases, and multi-turn conversations without truncation, with DeepSeek Sparse Attention optimizing long-sequence efficiency.
- Reasoning & Chain-of-Thought: Native support for extended thinking mode with chain-of-thought reasoning, verification patterns, and reflection capabilities built directly into R1 and the general-chat model line for complex problem-solving.
- Cost-Effective Token Pricing: Token-metered, pay-as-you-go API billing with cache-hit and off-peak rate discounts that undercut most closed frontier-class APIs on a per-token basis; see the pricing FAQ for exact current rates.
- Open-Source & Commercial Use: MIT-licensed open-source model weights available on GitHub and Hugging Face for self-hosting, fine-tuning, and commercial deployment without licensing restrictions or vendor lock-in.
- DeepSeek V4 (General Availability): DeepSeek V4 exited preview into general availability on July 20, 2026, as a two-model family: V4-Pro (1.6T total, 49B active) and V4-Flash (284B total, 13B active), both with 1M-token context windows, plus stronger agentic, math, and code-generation performance over the April preview.
Pros
- Reviewers and cost-conscious teams consistently point to DeepSeek's per-token API pricing as the main reason to switch from GPT-4-class or Claude-class services.
- The 128K-token context window lets it process full codebases or long documents in a single pass, something shorter-context rivals still have to chunk.
- MIT licensing means teams can self-host, audit, or fine-tune the weights instead of depending on a single vendor's uptime and terms.
- Only 37 of its 671 billion parameters activate per token, which is why it can run inference at a fraction of the cost of similarly capable dense models.
Cons
- Knowledge cutoff limited to September 2025, lacking real-time information and current events awareness.
- Less aligned than frontier models on safety and jailbreak benchmarks per Microsoft research; requires content filtering for production.
- Reasoning models consume more tokens than competitors' implementations, reducing token efficiency despite lower per-token costs.
- Geopolitical constraints and data governance concerns as a Chinese company subject to local regulatory oversight.
Frequently Asked Questions
How much does DeepSeek cost in 2026?
DeepSeek's web and mobile chat apps are free. API pricing is pay-as-you-go: DeepSeek-V3.2 runs about $0.28 per million input tokens and $0.42 per million output tokens, while R1 costs $0.55 and $2.19 respectively. Cache hits cut the input rate by 90%, and dedicated enterprise deployments start around $18,000 a year.
Is DeepSeek free to use?
Yes. The web and app chat interface is free with generous usage limits, and the API's free allowance covers up to 1 million input tokens a month with a limited amount of free output before metered billing kicks in.
What are the best alternatives to DeepSeek?
Qwen is the strongest pick if you want Alibaba Cloud integration and native Chinese-language performance. Mistral Large 3 suits teams that specifically want Apache 2.0 licensing instead of MIT. Kimi K3 is worth a look if you want the largest open-weight parameter count and a top open GPQA Diamond score.
How does DeepSeek compare to Qwen in 2026?
Both are Chinese-built, openly licensed models competing hard on cost, but they lean different ways. DeepSeek's reasoning and general-chat models push harder on math, coding, and multi-step reasoning benchmarks, while Qwen leans on deep Alibaba Cloud integration and stronger native Chinese-language handling. Teams already on Alibaba Cloud or serving Chinese-language users tend to pick Qwen; teams chasing the best reasoning-per-dollar tend to pick DeepSeek.
How do you get started with DeepSeek?
Sign up free at chat.deepseek.com or the mobile app to try the models with no setup. For programmatic access, create an API key at platform.deepseek.com and point any OpenAI SDK-compatible client at DeepSeek's endpoint, since the API is a drop-in replacement for OpenAI's chat completions format. Full docs, quick-start guides, and the open-weight model files are linked from DeepSeek's GitHub and Hugging Face pages.
Top Alternatives
- Qwen: Pick DeepSeek for coding benchmarks; pick Qwen for Chinese language and Alibaba ecosystem.
- Mistral Large 3: Pick DeepSeek for cheaper API pricing; pick Mistral Large 3 if you need Apache 2.0 weights instead of MIT.
- Kimi K3: Pick DeepSeek for lower per-token cost; pick Kimi K3 for the larger open-weight parameter count and top open GPQA score.
HokAI guides covering DeepSeek
- What Qwen Is Actually For, Now It's Priced Below Claude and GPT-5.6: Alibaba priced Qwen3.8-Max at $2/$6 per million tokens, beating Claude and GPT-5.6, but its benchmarks are self-reported and the open-weights license isn't out.
- Cursor Composer 2.5 vs. Claude Code: Which AI Coding Agent Should You Use in 2026?: Cursor Composer 2.5 vs Claude Code compared on 2026 pricing, benchmarks, and context windows now that Claude Code defaults to Sonnet 5 and Opus 5, not Opus 4.6.
- How to Choose the Right LLM: A Practical Guide to GPT, Claude, Gemini, Llama, DeepSeek, and Perplexity: Choosing an LLM in August 2026 means new GPT-5.6 pricing, an expiring Claude Sonnet 5 discount, and Meta exiting open-weight models. This guide has the numbers.