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About the Provider

DeepSeek is a Chinese AI research company based in Hangzhou focused on building frontier open-source language models. The company emphasizes open innovation in AI, publishing models and research under permissive licenses to make powerful language models widely accessible and support collaborative development in the global AI community.

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Model Quickstart

This section helps you quickly get started with the deepseek-ai/DeepSeek-V3.2 model on the Qubrid AI inferencing platform. To use this model, you need:
  • A valid Qubrid API key
  • Access to the Qubrid inference API
  • Basic knowledge of making API requests in your preferred language
Once authenticated with your API key, you can send inference requests to the deepseek-ai/DeepSeek-V3.2 model and receive responses based on your input prompts. Below are example placeholders showing how the model can be accessed using different programming environments.
You can choose the one that best fits your workflow. 
from openai import OpenAI

# Initialize the OpenAI client with Qubrid base URL
client = OpenAI(
    base_url="https://platform.qubrid.com/v1",
    api_key="QUBRID_API_KEY",
)

# Create a streaming chat completion
stream = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-V3.2",
    messages=[
      {
        "role": "user",
        "content": "Explain quantum computing in simple terms"
      }
    ],
    max_tokens=8192,
    temperature=1,
    top_p=0.95,
    stream=True
)

# If stream = False comment this out
for chunk in stream:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)
print("\n")

# If stream = True comment this out
print(stream.choices[0].message.content)

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Model Overview

DeepSeek-V3.2 is DeepSeek’s frontier open-source model with 685B total parameters and novel DeepSeek Sparse Attention (DSA) that reduces long-context computational cost by 50%.
  • Trained with a scalable RL framework, it achieves performance comparable to GPT-5, earning gold-medal results at the 2025 IMO and IOI competitions.
  • The model integrates reasoning and tool-use capabilities through large-scale agentic synthesis, making it suitable for complex multi-step workflows and long-horizon automation.

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Model at a Glance

FeatureDetails
Model IDdeepseek-ai/DeepSeek-V3.2
ProviderDeepSeek
ArchitectureMixture-of-Experts (MoE) Transformer with DeepSeek Sparse Attention (DSA) — 685B total parameters
Model Size685B Total
Parameters4
Context Length128K Tokens
Release Date2025
LicenseMIT
Training DataLarge-scale multilingual dataset with scalable RL framework for reasoning and agentic tool-use

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When to use?

You should consider using DeepSeek-V3.2 if:
  • You need advanced reasoning and complex agent tasks
  • Your application requires long-horizon agentic tool use
  • You are solving mathematical competition-level problems (IMO/IOI)
  • Your use case involves code generation and complex debugging
  • You need enterprise automation with integrated reasoning
  • Your workflow involves long-context document analysis

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Inference Parameters

Parameter NameTypeDefaultDescription
StreamingbooleantrueEnable streaming responses for real-time output.
Temperaturenumber1Recommended 1.0 for optimal performance.
Max Tokensnumber8192Maximum number of tokens to generate.
Top Pnumber0.95Controls nucleus sampling.

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Key Features

  • DeepSeek Sparse Attention (DSA): Novel attention mechanism that reduces long-context computational cost by 50% versus standard attention.
  • GPT-5-Class Performance: Achieves frontier-level results on reasoning benchmarks, matching the performance of proprietary closed models.
  • IMO & IOI Gold Medal: Earns gold-medal-level results at the 2025 International Mathematical Olympiad and International Olympiad in Informatics.
  • Integrated Reasoning & Tool Use: Reasoning capabilities are synthesized directly into agentic tool-use workflows via large-scale RL training.
  • 685B MoE Architecture: Efficient sparse inference enabling frontier capability with manageable active parameter counts.
  • MIT License: Fully open-source with unrestricted commercial use.

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Limitations

  • 128K Max Context: Shorter context window compared to some competing frontier models.
  • Infrastructure Requirements: Requires H100/H200-class GPU infrastructure for full deployment.
  • No Official Chat Template: No official Jinja chat template — custom encoding required for direct use.
  • Tool Calling Warm-up: Function calling may need a warm-up phase on cold-start deployments.

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Summary

DeepSeek-V3.2 is DeepSeek’s most capable open-source model, delivering GPT-5-class performance with efficient sparse inference.
  • It uses a 685B MoE Transformer with DeepSeek Sparse Attention, cutting long-context compute costs by 50%.
  • It achieves gold-medal results at IMO 2025 and IOI 2025, with integrated reasoning and tool-use via RL synthesis.
  • The model supports 128K context, streaming inference, and agentic workflows with function calling.
  • Licensed under MIT for full commercial use.