DeepSeek V3.2

DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism that reduces training and inference cost while preserving quality in long-context scenarios. A scalable reinforcement learning post-training framework further improves reasoning, with reported performance in the GPT-5 class, and the model has demonstrated gold-medal results on the 2025 IMO and IOI. V3.2 also uses a large-scale agentic task synthesis pipeline to better integrate reasoning into tool-use settings, boosting compliance and generalization in interactive environments.

DeepSeek V3.2 Exp

DeepSeek-V3.2-Exp introduces DeepSeek Sparse Attention (DSA) for efficient long-context. Reasoning toggle supported via boolean flag.

DeepSeek V3.1

DeepSeek V3.1 model integrated via automation on 2025-08-21

DeepSeek R1 0528

DeepSeek R1 0528 is the May 28th update to the original DeepSeek R1. Performance on par with OpenAI o1, but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inference pass. Fully open-source model.

DeepSeek Prover V2

A 671B parameter model, speculated to be geared towards logic and mathematics. Likely an upgrade from DeepSeek-Prover-V1.5. Released on Hugging Face without an announcement or description.

DeepSeek R1

DeepSeek R1 is a reasoning model developed entirely via reinforcement learning, offering cost efficiency at $0.14/million tokens vs. OpenAI o1's $15, with strong code generation and analysis capabilities.

DeepSeek V3.2

DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism that reduces training and inference cost while preserving quality in long-context scenarios. A scalable reinforcement learning post-training framework further improves reasoning, with reported performance in the GPT-5 class, and the model has demonstrated gold-medal results on the 2025 IMO and IOI. V3.2 also uses a large-scale agentic task synthesis pipeline to better integrate reasoning into tool-use settings, boosting compliance and generalization in interactive environments.

DeepSeek V3.2 Exp

DeepSeek-V3.2-Exp introduces DeepSeek Sparse Attention (DSA) for efficient long-context. Reasoning toggle supported via boolean flag.

DeepSeek V3.1

DeepSeek V3.1 model integrated via automation on 2025-08-21

DeepSeek R1 0528

DeepSeek R1 0528 is the May 28th update to the original DeepSeek R1. Performance on par with OpenAI o1, but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inference pass. Fully open-source model.

DeepSeek Prover V2

A 671B parameter model, speculated to be geared towards logic and mathematics. Likely an upgrade from DeepSeek-Prover-V1.5. Released on Hugging Face without an announcement or description.

DeepSeek R1

DeepSeek R1 is a reasoning model developed entirely via reinforcement learning, offering cost efficiency at $0.14/million tokens vs. OpenAI o1's $15, with strong code generation and analysis capabilities.