DeepSeek V3.2 Exp
Verified671B open-weight LLM built for long-context text tasks.
About DeepSeek V3.2 Exp
DeepSeek V3.2 Exp uses a 671B parameter architecture released under open weights by the DeepSeek team. Its design emphasizes accessibility for custom deployments while maintaining a very large parameter count suited to demanding workloads.
The model's standout feature is its 163840-token context window, enabling it to handle extended documents and multi-turn conversations without truncation. This capacity supports applications where retaining full context across lengthy inputs is essential.
Typical usage includes local or cloud-based inference, fine-tuning for specialized domains, and integration into research pipelines. Users leverage its open nature to experiment with large-scale text generation and analysis tasks.
Capabilities
How DeepSeek V3.2 Exp compares
DeepSeek V3.2 Exp (striped bar) vs other language models on intelligence, speed and price.
Price
USD per 1M output tokens · Lower is better · DeepSeek V3.2 Exp ranks #22 of 66
Sources: Artificial Analysis (intelligence, speed) · OpenRouter (price).
Best for
Processing extensive technical documentation
The 163840-token context enables thorough analysis and reasoning across long technical texts without losing coherence.
Debugging large codebases
Strong code generation and debugging capabilities support identifying issues and suggesting fixes in complex software projects.
Tackling advanced math and logic tasks
Mathematical and logical reasoning strengths make it effective for step-by-step problem solving in quantitative domains.
Strengths & limitations
Strengths
- +Strong coding performance
- +Effective handling of large contexts
- +Solid STEM reasoning
- +Efficient inference
Limitations
- –Text-only modality
- –No built-in vision or multimodal support
- –Standard LLM hallucination risks
Cost calculator
Estimate what DeepSeek V3.2 Exp would cost for your usage.
Based on DeepSeek V3.2 Exp's $0.27/1M input · $0.41/1M output. Estimate only — actual cost varies by provider and caching.
Quick start
OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. Only the model slug changes between models.
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://openrouter.ai/api/v1",
apiKey: process.env.OPENROUTER_API_KEY,
});
const completion = await client.chat.completions.create({
model: "deepseek/deepseek-v3.2-exp",
messages: [{ role: "user", content: "Hello!" }],
});
console.log(completion.choices[0].message.content);Model slug: deepseek/deepseek-v3.2-exp
Editor's verdict
DeepSeek V3.2 Exp is DeepSeek's open-weight language models with a 164K-token context window.
At $0.41 per 1M output tokens, it is very cost-efficient for its class.
As an open-weight model you can self-host it or call it through a hosted API.
Best suited to strong coding performance and effective handling of large contexts.
Frequently asked questions
The model provides a context window of 163840 tokens.
User reviews
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