DeepSeek V3.2 Exp

About DeepSeek V3.2 Exp

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.

Model Specifications

💰 Real-World Cost Examples

Estimated monthly costs for common use cases

DeepSeek Model Lineup

Compare all models from DeepSeek to find the best fit

Similar Models from Other Providers

Cross-brand alternatives with similar capabilities

Gemma 3 27B

Google

Input: $0.08

Output: $0.45

Context: 262k

Hermes 4 70B

Nous Research

Input: $0.13

Output: $0.40

Context: 131k

Nemotron 3 Super

NVIDIA

Input: $0.17

Output: $0.36

Context: 1.0M

Tongyi DeepResearch 30B A3B

Alibaba Qwen

Input: $0.09

Output: $0.45

Context: 131k

💡 Cheaper Alternatives

Same Brand (DeepSeek)

Cross Brand

🚀 Quick Start

Get started with DeepSeek V3.2 Exp API

from openai import OpenAI

client = OpenAI(
    base_url="https://api.provider.com/v1",
    api_key="YOUR_API_KEY"
)

response = client.chat.completions.create(
    model="deepseek/deepseek-v3.2-exp",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)
print(response.choices[0].message.content)

Continue Your Decision