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.
api_example.sh
Technical Specifications
Model Architecture & Performance
Pricing
Pay-per-use, no commitments
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
Enterprise
Platform Integration
Docker Support
Official Docker images for containerized deployments
Kubernetes Ready
Production-grade KBS manifests and Helm charts
SDK Libraries
Official SDKs for Python, Javascript, Go, and Java
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