DeepSeek broke an assumption the AI industry had been living on: that frontier reasoning required frontier prices. The Chinese lab’s models match or approach the big assistants on math, code and logic benchmarks, ship under open licences, and cost a fraction of competing APIs. The result is an assistant that has become the default second opinion for developers and the budget option for high-volume automation.
This review covers what DeepSeek offers, how the reasoning mode behaves in practice, pricing, the privacy and censorship questions that come with it, and where it fits next to the assistants you already use.
What DeepSeek is
DeepSeek is both a free AI assistant and a family of open-weight models from Hangzhou-based DeepSeek AI. The chat app and web version work like any modern assistant: conversation, file upload, web search and image understanding. Underneath sit the models, most recently the V3.2 line with a context window around one million tokens, plus the dedicated R1 reasoning models that think step by step before answering.
The differentiator is openness. Model weights are published under permissive licences, which means anyone can run DeepSeek locally, fine-tune it, or build on it, and an ecosystem of local deployments has grown around exactly that. API pricing is set aggressively below the Western frontier labs while benchmark performance sits close behind.
Where it performs
Reasoning and math. The R-style reasoning mode works through competition math, logic puzzles and multi-step analysis with visible chains of thought, competing with far more expensive models.
Coding. Code generation, debugging and refactoring are strengths, and the low API cost makes agentic coding tools economically viable at volume, which is why many developer tools added DeepSeek as a model option.
Long documents. The million-token context ingests books, codebases and long contract sets in one pass, and the pricing makes that practical rather than a stunt.
Cost at scale. Per-million-token prices are typically an order of magnitude below equivalent Western APIs, which changes what is affordable for batch jobs, agents and startups.
A workflow that gets good answers
1. Use reasoning mode for hard problems
Switch on the thinking mode for math, logic and multi-constraint questions. For quick lookups and casual drafting it just adds latency.
2. Give context, not vibes
DeepSeek rewards precise prompts: paste the actual code, the actual constraints, the actual data. Long context is cheap, so use it.
3. Ask for the reasoning, then the answer
For analysis tasks, request the chain of thought and check it. Reasoning models are most trustworthy when you can audit the path, not just the destination.
4. Keep sensitive data out of the cloud chat
If privacy matters, use the open weights locally or through a self-hosted deployment rather than sending documents to the hosted service.
5. Cross-check consequential facts
Like every LLM, it can state wrong facts confidently. Treat it as a reasoning engine, and verify load-bearing claims independently.
What it costs
The web and mobile assistant is free, including reasoning mode, file uploads and search. There is no usage cap that matters for ordinary chat.
API pricing is where DeepSeek distinguishes itself: input and output tokens cost a small fraction of comparable frontier models, with off-peak discounts and caching reducing costs further. Exact figures change with each release, but the pattern has held since the R1 release: comparable benchmark performance at roughly a tenth of the price. Running the open weights yourself transfers the cost to hardware, and smaller distilled versions run on consumer GPUs.
Limitations to check
Privacy and jurisdiction are the serious considerations. The hosted service stores data under Chinese law, and several governments and companies restrict its use for sensitive work. The models also follow Chinese content regulations, so politically sensitive topics are filtered in ways other assistants are not, and creative writing can be noticeably constrained.
Operationally, the service has had capacity problems during demand spikes, and the ecosystem of third-party providers hosting the weights varies in quality. English performance is strong but slightly behind the frontier on nuanced writing; its clearest edge is logic, math and code. And because weights are open, third-party fine-tunes differ in behavior from the official service.
How it compares
Against ChatGPT and Claude, DeepSeek trades polish, ecosystem integrations and content permissiveness for price and openness. Against Ollama users, it is one of the best open-weight families to actually run locally. For privacy-critical workloads, self-hosting the weights is the sensible configuration; for everyone else, the hosted chat is a free way to test whether the reasoning quality fits your work before deciding where it belongs in your stack.
Frequently asked questions
Is DeepSeek free to use? Yes, the web and mobile assistant is free, including reasoning mode. API usage is pay-per-token at low rates.
Is DeepSeek open source? The model weights are published under permissive licences and can be run locally, though the hosted service itself is not open.
Is DeepSeek safe for confidential work? Be careful with the hosted service. For confidential data, run the open weights on your own infrastructure.
How does DeepSeek compare to ChatGPT? Comparable reasoning and coding at far lower cost, with fewer integrations, less content flexibility and different data governance.
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