DeepSeek: DeepSeek V3.2 Exp
By Deepseek
Released: 2025-09-29
Deepseek Deepseek V3 2 Exp, developed by DeepSeek, features 164k-token context window. DeepSeek-V3.2-Exp is an experimental large language model released by DeepSeek as an intermediate step between V3.1 and future architectures. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism designed to improve training and inference efficiency in long-context scenarios while maintaining output quality. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config) The model was trained under conditions aligned with V3.1-Terminus to enable direct comparison. Benchmarking shows performance roughly on par with V3.1 across reasoning, coding, and agentic tool-use tasks, with minor tradeoffs and gains depending on the domain. This release focuses on validating architectural optimizations for extended context lengths rather than advancing raw task accuracy, making it primarily a research-oriented model for exploring efficient transformer designs. Available at $0.21/1M tokens.
Visit DeepSeek: DeepSeek V3.2 ExpAI-Powered
Leverages advanced AI technology to deliver cutting-edge capabilities and results.
Fast & Efficient
Optimized performance ensures quick results without compromising on quality.
Purpose-Built
Specifically designed for llms tasks and workflows.
Deepseek Model Timeline
164k tokens context
164k tokens context
164k tokens context
164k tokens context
164k tokens context
33k tokens context
164k tokens context
164k tokens context
164k tokens context
33k tokens context
131k tokens context
64k tokens context
164k tokens context
Specifications
AI Evaluation
Combines language understanding with search capabilities. Excels at finding and synthesizing information from various sources.
Pros
- Competitive pricing ($0.21/1M)
- 164k token context window
- Strong code generation and debugging
- Advanced logical reasoning
Cons
- May lack creative flair
- Preview release - may change
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