DeepSeek: DeepSeek V3.2 Exp

deepseek/deepseek-v3.2-exp

Created Sep 29, 2025163,840 context

$0.21/M input tokens$0.32/M output tokens

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

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.

Providers for DeepSeek V3.2 Exp

OpenRouter routes requests to the best providers that are able to handle your prompt size and parameters, with fallbacks to maximize uptime.

Performance for DeepSeek V3.2 Exp

Compare different providers across OpenRouter

Apps using DeepSeek V3.2 Exp

Top public apps this month

Recent activity on DeepSeek V3.2 Exp

Total usage per day on OpenRouter

Prompt

1.29B

Completion

104M

Reasoning

17.4M

Prompt tokens measure input size. Reasoning tokens show internal thinking before a response. Completion tokens reflect total output length.

Uptime stats for DeepSeek V3.2 Exp

Uptime stats for DeepSeek V3.2 Exp across all providers

Sample code and API for DeepSeek V3.2 Exp

OpenRouter normalizes requests and responses across providers for you.

OpenRouter supports reasoning-enabled models that can show their step-by-step thinking process. Use the reasoning parameter in your request to enable reasoning, and access the reasoning_details array in the response to see the model's internal reasoning before the final answer. When continuing a conversation, preserve the complete reasoning_details when passing messages back to the model so it can continue reasoning from where it left off. Learn more about reasoning tokens.

In the examples below, the OpenRouter-specific headers are optional. Setting them allows your app to appear on the OpenRouter leaderboards.

Using third-party SDKs

For information about using third-party SDKs and frameworks with OpenRouter, please see our frameworks documentation.

See the Request docs for all possible fields, and Parameters for explanations of specific sampling parameters.