DeepSeek V3.2 Exp

DeepSeek V3.2 Exp by DeepSeek excels in the Coding category, where it ranks #93 with a composite score of 53/100. 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. It is particularly strong in areas highlighted by its top benchmark performance and adoption metrics, making it suitable for both individual developers and enterprise teams looking for a reliable coding solution.

DeepSeek V3.2 Exp is priced at $0.27 per million input tokens and $0.41 per million output tokens (USD). Contact the provider for volume discounts and enterprise pricing. Pricing is competitive within the coding category and reflects the model's quality-to-cost ratio.

In the Coding category, DeepSeek V3.2 Exp holds rank #93 out of 6 models tracked. Its quality rank is #93 and adoption rank is #93. You can use our comparison tool at /compare to see detailed side-by-side metrics with specific alternatives. Key differentiators include its composite scoring across benchmarks, community sentiment, and real-world adoption rates.

DeepSeek V3.2 Exp has been evaluated across 6 different signals. Its strongest areas include Capabilities (57/100), Context Window (83/100), Output Capacity (80/100). These scores are derived from industry-standard benchmarks, community ratings, and real-world performance metrics. The composite score of 53/100 reflects a weighted combination of all tracked signals.

DeepSeek V3.2 Exp is a paid model, though some providers may offer trial credits or limited free tiers for evaluation. Check DeepSeek's website for current free tier availability and promotional offers.