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deepseek-ai/DeepSeek-V3

DeepSeek-V3 is a 671B-parameter Mixture-of-Experts model (37B active) that combines Multi-head Latent Attention with multi-token prediction to deliver fast, cost-efficient reasoning over a 128K context window.

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",
  "messages": [
    {
      "role": "user",
      "content": "Explain quantum computing in simple terms"
    }
  ],
  "temperature": 0.7,
  "max_tokens": 4096,
  "stream": true,
  "top_p": 1
}'

Pricing

Pay-per-use, no commitments

Input Tokens $0.30/1M Tokens
Output Tokens $1.30/1M Tokens
Cached Input Tokens $0.00/1M Tokens

Technical Specifications

Model Architecture & Performance

Variant Chat (non-thinking)
Model Size 671B params (37B active)
Context Length 128K Tokens
Quantization FP8 / BF16
Tokens/sec 60
Architecture Mixture-of-Experts Transformer with Multi-head Latent Attention; 256 experts with top-8 routing per token
Precision FP8 mixed precision
License MIT License
Release Date December 2024
Developers DeepSeek-AI

API Reference

Complete parameter documentation

Parameter Type Default Description
stream boolean true Enable streaming responses for real-time output.
temperature number 0.7 Controls randomness.
max_tokens number 4096 Maximum number of tokens to generate.
top_p number 1 Controls nucleus sampling.
enable_thinking boolean false Toggle chain-of-thought reasoning mode. Set temperature=1.0 when enabled.

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

Performance

Strengths & considerations

Strengths Considerations
671B MoE with only 37B parameters active per token for efficient inference
Multi-token prediction and MLA routing improve throughput and memory usage versus dense models
Pre-trained on ~14.8T diverse tokens followed by extensive SFT and RL alignment
Advertised 60 tokens/second throughput enables responsive interactive applications
Self-hosting the 128K context variant requires 261GB (INT4) to 1.66TB (FP16) of VRAM, limiting on-prem deployments
FP8 or INT4-capable hardware is required to replicate vendor-reported efficiency
Massive parameter count still drives higher power and infrastructure cost than smaller DeepSeek releases

Use cases

Recommended applications for this model

High-throughput coding and reasoning agents that benefit from 60 token-per-second streaming output
Research copilots synthesizing long technical or financial documents within the 128K token context
Multilingual chat or analysis workloads that need open weights with favorable cost-performance

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