deepseek-ai/DeepSeek-V3.2

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. The model includes reasoning and tool-use capabilities through large-scale agentic synthesis.

DeepSeek Chat 128K Tokens
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api_example.sh

curl -X POST "https://platform.qubrid.com/v1/chat/completions" \
  -H "Authorization: Bearer QUBRID_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "deepseek-ai/DeepSeek-V3.2",
  "messages": [
    {
      "role": "user",
      "content": "Explain quantum computing in simple terms"
    }
  ],
  "temperature": 1,
  "max_tokens": 8192,
  "stream": true,
  "top_p": 0.95
}'

Technical Specifications

Model Architecture & Performance

Variant Instruct
Model Size 685B params
Context Length 128K Tokens
Quantization FP8
Tokens/Second 45
Architecture DeepSeek Sparse Attention (DSA) MoE Transformer — 685B total parameters, 256 experts per layer (8 activated per token), MLA attention
Precision FP8 / bf16
License MIT License
Release Date December 2025
Developers DeepSeek-AI

Pricing

Pay-per-use, no commitments

Input Tokens $0.00056/1K Tokens
Output Tokens $0.00168/1K Tokens

API Reference

Complete parameter documentation

Parameter Type Default Description
stream boolean true Enable streaming responses for real-time output.
temperature number 1 Recommended 1.0 for optimal performance.
max_tokens number 8192 Maximum number of tokens to generate.
top_p number 0.95 Controls nucleus sampling.

Explore the full request and response schema in our external API documentation

Performance

Strengths & considerations

Strengths Considerations
DeepSeek Sparse Attention — 50% compute savings on long contexts
GPT-5-class performance on reasoning benchmarks
Gold-medal IMO 2025 and IOI 2025 performance
685B MoE with efficient inference
Integrated reasoning into tool-use via RL synthesis
MIT License — fully open source
128K max context window
Requires H100/H200 class infrastructure for full deployment
No official Jinja chat template — custom encoding required
Tool calling may need warm-up on cold-start phases

Use cases

Recommended applications for this model

Advanced reasoning & agent tasks
Long-horizon agentic tool use
Mathematical competition problems (IMO/IOI level)
Code generation and complex debugging
Enterprise automation
Long-context document analysis

Enterprise
Platform Integration

Docker

Docker Support

Official Docker images for containerized deployments

Kubernetes

Kubernetes Ready

Production-grade KBS manifests and Helm charts

SDK

SDK Libraries

Official SDKs for Python, Javascript, Go, and Java

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