DeepSeek-V3.2-Speciale

DeepSeek-V3.2-Speciale has an architecture identical to the base DeepSeek-V3.2 version. The main difference between Speciale and the base version is its exclusive focus on reasoning capabilities, without support for agent functions or tool usage, as explicitly stated in the model card and technical documentation. During the reinforcement learning stage, it was trained exclusively on reasoning data with relaxed constraints on the length of reasoning generation. The dataset and reward methodology from DeepSeekMath-V2 were integrated into the training process to enhance its ability to derive mathematical proofs. This allowed the model to generate significantly longer chains of "thoughts," which is critical for solving Olympiad-level problems.

By sacrificing agent capabilities, the model set new standards for LLMs on the most challenging benchmarks. On AIME 2025, Speciale achieved 96.0% accuracy compared to 95.0% for Gemini-3.0-Pro and 94.6% for GPT-5. On HMMT February 2025, the result was 99.2%, which also surpasses all competing models. In the Codeforces rating, the model reached 2701—almost the same as Gemini-3.0-Pro (2708), while other models perform significantly lower. On LiveCodeBench, the model scored 88.7%, thus being second only to Gemini-3.0-Pro (90.7%). On IMOAnswerBench (problems at the level of the International Mathematical Olympiad), the result was

DeepSeek-V3.2-Speciale is recommended for research scenarios where token efficiency is secondary to solution quality, meaning it is intended for specific tasks requiring extremely deep reasoning: solving Olympiad-level mathematical problems (IMO, CMO, AIME, HMMT), competitive programming (Codeforces, IOI, ICPC), complex proofs and verification, combinatorial and algorithmic problems with extended solution search, and scientific research requiring multi-step logical inference. The model is not suitable for agent scenarios, working with tools, or production environments.

Announce Date: 01.12.2025
Parameters: 685B
Experts: 256
Activated at inference: 37B
Context: 164K
Layers: 61
Attention Type: DeepSeek Sparse Attention
Developer: DeepSeek
Transformers Version: 4.44.2
License: MIT

Use our pre-built public endpoints for free to test inference and explore DeepSeek-V3.2-Speciale capabilities. You can obtain an API access token on the token management page after registration and verification.

An endpoint is a server with pre-installed software that automatically downloads neural network weights and launches an OpenAI-compatible API to handle incoming requests.

A private endpoint is created within the user's project, belongs exclusively to the user, and is billed per server configuration in accordance with the infrastructure pricing.

We recommend creating private endpoints when you need to:

• achieve maximum endpoint performance,
• access the full context window for processing large requests and conversations,
• ensure maximum security by processing data in a dedicated, isolated environment.

Contact our dedicated neural networks support team at support@immers.cloud or send your request to the sales department at sale@immers.cloud.