What Is DeepSeek? The Independent Guide for 2026

If you have searched “what is DeepSeek”, you have probably hit a wall of breathless headlines and very little plain-English explanation. This guide fixes that. DeepSeek is a Hangzhou-based AI lab that builds open-weight large language models, distributes them on Hugging Face, and runs a low-cost API that mirrors the OpenAI and Anthropic wire formats. As of October 2026 its API serves two models, both with a one-million-token context window: deepseek-flash (DeepSeek-V4.1-Flash-1017, released on 17 October 2026) and deepseek-v4-pro (DeepSeek-V4-Pro-0813, released on 13 August 2026). By the end of this article you will know what the company makes, how the models differ, what you pay per million tokens, and where DeepSeek genuinely competes — and where it does not.

DeepSeek in one paragraph

DeepSeek is a Chinese AI research lab, founded in 2023 and backed by the quantitative hedge fund High-Flyer, that develops open-weight foundation models. It gained attention in late 2024 with its free, open-weight V3 model, which it said was trained with less powerful chips and at a fraction of the cost of models built by the likes of OpenAI and Google. Weeks later, in January 2025, it released a reasoning model, R1, whose reported benchmark results put it alongside leading closed models of the time. The company publishes its weights on Hugging Face (licences vary by model, see below), ships detailed technical reports, and runs a developer API at https://api.deepseek.com that is compatible with both the OpenAI Chat Completions and Anthropic SDK formats. If you want the short answer to “what is DeepSeek”: it is a frontier-adjacent model maker that competes primarily on price, openness and long-context efficiency rather than on raw benchmark leadership.

The current DeepSeek models

DeepSeek’s current generation began with the V4 Preview on 24 April 2026. Since then the preview models have been replaced by official releases: DeepSeek-V4-Pro-0813 on 13 August 2026, DeepSeek-V4.1-Flash on 10 September 2026 and its minor update DeepSeek-V4.1-Flash-1017 on 17 October 2026. On the API, thinking mode is controlled by a request parameter rather than a separate model ID, and both models support it (it is on by default).

Both are open weights, downloadable from Hugging Face: DeepSeek-V4.1-Flash-1017 and DeepSeek-V4-Pro-0813. DeepSeek does not state parameter counts for either current model; the figures often quoted (1.6T total / 49B active for V4-Pro, 284B / 13B for V4-Flash) belong to the April 2026 preview. For deeper architectural detail, see our breakdown of DeepSeek V4, the per-tier write-ups for DeepSeek V4-Pro and DeepSeek V4-Flash, or browse all DeepSeek models on the DeepSeek models hub.

Why two models instead of one

The split is pragmatic. deepseek-flash is the cheaper default for chat, summarisation, RAG and standard coding work, and the only current API model that accepts images. deepseek-v4-pro costs 3.3 times as much on output and 4.4 times as much on cache-miss input. Which one performs better is less clear-cut than the names suggest: DeepSeek’s 10 September 2026 announcement said tests by multiple parties put V4.1-Flash ahead of V4-Pro on performance, cost, speed and total runtime. DeepSeek nevertheless kept V4-Pro on the API after 14 September 2026, and its pricing page lists both models today.

Where DeepSeek came from — a short lineage

• 2023 — DeepSeek founded in Hangzhou by Liang Wenfeng, spun out of the High-Flyer quant fund.
• Late 2024 — DeepSeek V3 released; its compute cost was reported at roughly $5.6M.
• January 2025 — DeepSeek R1 reasoning model released. R1’s training cost was later disclosed as $294,000 (Reuters, September 2025).
• August–December 2025 — V3.1 (one model with thinking and non-thinking modes, toggled by a “DeepThink” button in the web chat at the time) and V3.2.
• 24 April 2026 — DeepSeek-V4 Preview ships as V4-Pro and V4-Flash.
• 13 August 2026 — DeepSeek-V4-Pro-0813, the official release of V4-Pro, together with peak and off-peak API pricing.
• 10 September 2026 — DeepSeek-V4.1-Flash, served as deepseek-flash with native image understanding.
• 17 October 2026 — DeepSeek-V4.1-Flash-1017, a minor update, plus Search and Fetch APIs opened to everyone.

If you want the long version, our DeepSeek history article walks through every major release with citations.

What DeepSeek actually does well

Coding and competitive programming

This is the area where DeepSeek’s own numbers are strongest. On the DeepSeek-V4.1-Flash-1017 model card (17 October 2026), DeepSeek reports 77.0 on DeepSWE v1.1, against 74.0 for Opus 5 and 73.0 for GPT-5.6 Sol in the same table, and 68.2 on NL2Repo-Bench. The DeepSeek-V4-Pro-0813 card (13 August 2026) reports 87.9 on Terminal Bench 2.1 and 62.7 on DeepSWE. The two cards use different benchmark versions, so their numbers should not be read as a head-to-head; all of them are DeepSeek-reported.

Long-context efficiency

V4 is built around the assumption that million-token prompts should be cheap, not just possible. The April 2026 V4 preview model card describes a hybrid attention mechanism combining Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA), and reports that in the 1M-token setting the preview V4-Pro needed only 27% of the single-token inference FLOPs and 10% of the KV cache of DeepSeek-V3.2. DeepSeek’s V4.1-Flash technical report is titled “Pushing the Limits of KV Cache Compression”. Both current API models accept up to 1M tokens of context, which makes feeding a large codebase or a very long contract into a single prompt a realistic option on cost.

Open weights

Every recent release (V4.1-Flash-1017, V4-Pro-0813, V4.1-Flash, V3.2, V3.1, R1) ships both code and model weights under MIT. Some older releases (V3 base, Coder-V2, VL2) split MIT-licensed code from a separate DeepSeek Model License for the weights, so check the specific repo if licensing matters to you. For a deeper read, see is DeepSeek open source.

Where DeepSeek falls short

Honesty matters here. DeepSeek is not the universal winner that some launch coverage implied.

• Closed frontier models still lead on some axes — in the same DeepSeek-reported table on the V4.1-Flash-1017 card, Opus 5 scores 56.3 on HLE without tools against 42.6 for V4.1-Flash-1017, 51.8 against 42.3 on Terminal-Bench 4.0, and 75.3 against 68.2 on NL2Repo-Bench.
• Regulatory friction — DeepSeek apps and services have been restricted in several jurisdictions; see DeepSeek availability by country.
• Privacy — conversations on the official chat and API are processed and may be stored on servers subject to Chinese law. Treat anything sensitive accordingly; DeepSeek privacy covers this in detail.

How you actually use DeepSeek

There are three surfaces, and they behave differently.

1. Web chat at chat.deepseek.com — keeps your conversation history with your account; no setup beyond an account. Browse DeepSeek chat for a walkthrough.
2. Mobile apps on iOS and Android — DeepSeek’s 17 October 2026 Change Log says the official web and app models were updated to DeepSeek-V4.1-Flash-1017, and its 13 August 2026 release notes said V4 Pro is available on the app and web via “Expert Mode”.
3. Developer API at https://api.deepseek.com — stateless, billed per token, OpenAI- and Anthropic-SDK compatible.

The crucial distinction for developers: chat requests hit POST /chat/completions, the OpenAI-compatible endpoint, and the API is stateless. The server does not remember previous turns; your client must resend the full conversation history with every request. This is the opposite of how the web chat behaves. In code you choose deepseek-flash or deepseek-v4-pro in the model field; setup, code samples and parameters are covered in the DeepSeek API documentation and the DeepSeek API getting started tutorial.

Pricing — what you actually pay

Prices as of October 2026, in USD per million tokens, from DeepSeek’s official pricing page. Off-peak rates are half the peak rates. Peak hours are 01:00–04:00 and 06:00–10:00 UTC, Monday to Friday, excluding Chinese public holidays; all other hours, including weekends, are off-peak.

For most chat and RAG workloads Flash is the default; reach for Pro only when a task genuinely justifies the higher rates. Worked examples and the full rate history are on the DeepSeek API pricing reference, and the DeepSeek pricing calculator will run the numbers for any token mix you feed it.

How DeepSeek compares to the alternatives

For a one-line summary: DeepSeek competes on price and openness. Closed frontier models from OpenAI, Anthropic and Google still hold edges on several benchmarks and on ecosystem polish, while DeepSeek’s own published numbers put its current models close to or ahead of them on some coding-agent benchmarks, and it publishes the weights. Compare its per-token rates against each vendor’s current pricing page before committing, since all of them change prices often.

Detailed head-to-heads live in DeepSeek vs ChatGPT and DeepSeek vs Claude; for a wider survey, see DeepSeek alternatives.

Who should use DeepSeek

• Developers running high-volume agentic coding workflows where output token cost dominates the bill.
• Researchers and engineers who need open weights for fine-tuning, on-prem deployment or audit.
• Teams that already speak the OpenAI SDK and want a near-drop-in second provider.
• Cost-sensitive teams shipping chat, summarisation or RAG at scale, especially workloads that can run off-peak.

Who probably should not: anyone with a hard requirement that data not be processed in China, anyone in a jurisdiction that has restricted DeepSeek, and anyone whose workload depends on the benchmarks where closed models still lead.

For role-specific guidance, see DeepSeek for developers or browse all DeepSeek beginner guides for entry points.

The honest verdict

DeepSeek is not the strongest AI lab by every measure, and its launch materials sometimes overstate the case. What it is, as of October 2026, is a credible open-weight challenger to the closed frontier — behind on several of the hardest benchmarks, close or ahead on some coding ones by its own published numbers, and priced low enough that trying it costs little. That combination is what the question “what is DeepSeek” really points at: a lab that has pushed down the price of serious AI while keeping the weights downloadable.