DeepSeek Models: Full List, Features & Use Cases

DeepSeek's model lineup has grown from a small open-source coding model into one of the most closely watched open-weight AI families in the world, known for near-frontier benchmark scores at a fraction of the price of closed competitors. This guide lists every major DeepSeek model in order from the newest to the oldest, with a concise overview of each model's specs, key features, and intended use — plus links to try the ones available on Chat Smith.

Released April 24, 2026 under the MIT license, DeepSeek V4 Pro (1.6T total / 49B active parameters) and DeepSeek V4 Flash (284B / 13B active) both ship with a 1M-token context window, made economical by a new hybrid attention design. Pricing: V4 Pro at $0.435/1M input, $0.87/1M output; V4 Flash at $0.14/1M input, $0.28/1M output. Best for: agentic coding and long-context reasoning (Pro), high-volume latency-sensitive tasks (Flash).

Released December 1, 2025, DeepSeek V3.2 was the first DeepSeek model to integrate thinking directly into tool use, scoring 73.1% on SWE-Bench Verified. Its high-compute sibling, V3.2-Speciale, pushed reasoning further, posting gold-medal-level olympiad math results and 96% on AIME. The DeepSeek Reasoner V3.2 endpoint exposes this reasoning behavior through a dedicated "thinking" mode. Best for: general Q&A and agent tasks (V3.2), competition-level math and long-form reasoning (Speciale).

Released September 29, 2025, V3.2-Exp introduced DeepSeek Sparse Attention (DSA), cutting API pricing roughly in half versus the prior generation while improving throughput on long-context tasks. It served as the experimental preview that shaped the full V3.2 release two months later. Best for: reference only — superseded by V3.2.

Released August 21, 2025, DeepSeek V3.1 merged "thinking" and regular response modes into a single model for the first time, removing the need to pick separate chat and reasoning endpoints. The September 22 Terminus refinement cleaned up bilingual (English/Chinese) output, strengthened agentic tool use, and made long-context handling cheaper. Best for: reference only — superseded by V3.2.

Released May 28, 2025, R1-0528 was a major update to DeepSeek's original reasoning model, jumping to 91 on AIME, 73 on LiveCodeBench, and 57.6% on SWE-bench Verified. DeepSeek also shipped an 8B distilled version based on Qwen3 small enough to run on a laptop. Best for: reference only — reasoning capability now lives inside the V3.2/V4 lineup.

Released March 24, 2025, V3-0324 was a quiet but significant update to the original V3 base model — a 685B-parameter MoE model released on Hugging Face under the MIT license, and the foundation later releases built on. Best for: reference only — fully superseded by V3.1 and later.

Released January 20, 2025, DeepSeek R1 is the release that made the company a household name, wiping hundreds of billions of dollars off US tech stocks the week it launched. It demonstrated that pure reinforcement-learning-driven reasoning, without heavy supervised fine-tuning, could produce a model competitive with OpenAI's o1. The experimental R1-Zero checkpoint tested RL from scratch, while R1 added cold-start data to improve readability. Six smaller distilled models let developers run a lighter version locally. Best for: reference only — succeeded by R1-0528 and the V3.2/V4 lineup.

Released December 25–26, 2024, DeepSeek V3 scaled DeepSeek's Mixture-of-Experts design to 671 billion total parameters (37B active), trained on 14.8 trillion tokens for a reported cost far below what Western labs were spending on comparable models. It's the base architecture that R1 was later built on. Best for: reference and research — self-hostable under a permissive license.

Released September 5, 2024 and revised that December, V2.5 merged DeepSeek's general-purpose chat model with its coding-focused line into a single model, simplifying the lineup ahead of V3. Best for: historical reference only.

Released May 6, 2024, DeepSeek V2 (236B total / 21B active parameters, 128K context) is the architectural starting point for every later DeepSeek flagship: it introduced Multi-head Latent Attention (MLA) for cheaper KV caching alongside a Mixture-of-Experts design, cutting training cost by 42.5% and KV cache size by 93.3% versus DeepSeek's earlier dense models. DeepSeek Coder V2 followed in June, extending the architecture to 338 programming languages. Best for: historical reference — the architecture, not the checkpoint, is what matters today.

Released in November 2023, DeepSeek's first models — DeepSeek Coder and the 7B/67B DeepSeek LLM — were trained on roughly 2 trillion tokens and were competitive with Meta's LLaMA-2 70B on code, math, and reasoning benchmarks at launch. They put DeepSeek on the map as a serious open-source lab well before V3 or R1 made global headlines. Best for: historical reference only.

DeepSeek's lineup now spans open-weight general models, dedicated reasoners, and a fast/pro split at the flagship tier — all released under permissive licenses that undercut closed competitors by an order of magnitude on price. For teams and individuals who want to use these models without self-hosting, Chat Smith gives you access to the available DeepSeek models alongside GPT, Claude, Gemini, and Grok — all from a single interface. Browse the full AI model catalog on Chat Smith to find the right model for your workflow.

1. What is the newest DeepSeek model in 2026?

As of July 2026, the newest DeepSeek models are V4 Pro and V4 Flash, released April 24, 2026. V4 Pro is the flagship for complex reasoning and agentic coding; V4 Flash is the faster, cheaper variant for high-volume tasks.

2. Which DeepSeek models are available on Chat Smith?

Chat Smith currently offers DeepSeek V4 Pro, DeepSeek V4 Flash, DeepSeek V3.2, and DeepSeek Reasoner V3.2. You can browse the full list on the Chat Smith model page.

3. What's the difference between DeepSeek's V-series and R-series models?

The V-series (V2, V3, V4) is DeepSeek's general-purpose model line for chat, coding, and everyday tasks. The R-series (R1) was a dedicated reasoning line built on top of a V-series base model. Since V3.1, DeepSeek has merged reasoning directly into the main V-series models, so the standalone R-series is no longer being actively developed.