DeepSeek

DeepSeek's first model release: a family of open-source code models from 1.3B to 33B parameters, trained from scratch on 2 trillion tokens and reported to beat Codex and GPT-3.5 on several benchmarks.

DeepSeek LLM is DeepSeek's first general-purpose language model series, released on 29 November 2023 in 7B and 67B sizes, each with a base version and a chat version.

DeepSeekMoE is a mixture-of-experts architecture from DeepSeek, released in January 2024, that splits experts finely and keeps a few shared experts always active so that each expert holds focused knowledge. Its 16B open-weights model matched larger dense models at a fraction of the computation.

DeepSeekMath is a 7B-parameter open language model built for mathematical reasoning and released in 2024. It reached 51.7% on the competition-level MATH benchmark without external tools, and introduced GRPO, a reinforcement-learning algorithm that uses less memory than PPO.

DeepSeek-V2 is a mixture-of-experts language model released in May 2024 with 236B total parameters, of which 21B are activated per token. It introduced Multi-head Latent Attention and used DeepSeekMoE for economical sparse training.

An open-source mixture-of-experts code model from DeepSeek, released in June 2024, that widened language coverage and context length over its predecessor and reported benchmark results above several closed models.

DeepSeek-V2.5 is a model released on 5 September 2024 that merged DeepSeek's chat and code models into one, keeping both conversational and coding ability while improving safety scores.

A family of open mixture-of-experts vision-language models from DeepSeek, released in three sizes from 1.0B to 4.5B activated parameters for document, chart and OCR understanding, succeeding DeepSeek-VL.

The DeepSeek app is the company's official free chatbot, offered on the web and as mobile apps since 15 January 2025. It topped the U.S. App Store in late January 2025.

Janus-Pro is DeepSeek's January 2025 multimodal model that handles both image understanding and text-to-image generation in one system, released in 1B and 7B sizes as a stronger successor to Janus.

DeepSeek-V3-0324 is a March 2025 update to DeepSeek-V3 that keeps the same model structure while raising benchmark scores and moving to the MIT License.

DeepSeek's open-source model for formal theorem proving in Lean 4, trained with a subgoal-decomposition pipeline; it reached an 88.9% pass ratio on MiniF2F-test and introduced the ProverBench benchmark.

An updated release of DeepSeek's open-weight reasoning model, published on 28 May 2025 under the MIT License, with markedly higher scores on competition math, science and coding benchmarks, plus system-prompt support and stronger function calling.

DeepSeek-V3.1, released on 21 August 2025 under the MIT License, was DeepSeek's first hybrid model, supporting both a thinking mode and a non-thinking mode in one set of weights. It has 671 billion total parameters, with 37 billion activated per token.

An experimental DeepSeek model released on 29 September 2025 that introduced DeepSeek Sparse Attention to make long-context training and inference cheaper, shipped with open weights and an immediate API price cut.

DeepSeek-OCR is an open-source model of about 3B parameters that renders long text as 2D images and reads it back from a small number of vision tokens, testing whether optical compression can stand in for very long text contexts.

DeepSeekMath-V2, released on 27 November 2025, is a mathematical reasoning model trained to verify its own proofs. DeepSeek reported gold-level scores on IMO 2025 and CMO 2024 problems, and 118 of 120 on Putnam 2024.

DeepSeek-V3.2, released on 1 December 2025, succeeded the experimental V3.2-Exp and integrated thinking into tool use. Its companion V3.2-Speciale, which maximizes reasoning, reached gold-level results at several 2025 competitions.

DeepSeek-V4.1-Flash, released on 10 September 2026 under the API name deepseek-flash, is a 552-billion-parameter multimodal mixture-of-experts model with a causal encoder-decoder design and a 1M-token context.