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DeepSeek: DeepSeek V3Scale Reasoning Efficiently

Try DeepSeek: DeepSeek V3API Documentation

Run V3 Smarter Faster

MoE Power

671B Total 37B Active

Activates 37B parameters per token from 671B MoE for efficient high performance.

Speed Boost

60 Tokens Second

Delivers 3x faster inference than V2 using MLA and DeepSeekMoE architectures.

Cost Efficient

2.8M GPU Hours

Trained on 14.8T tokens with stable process cutting memory use by 50%.

Examples

See what DeepSeek: DeepSeek V3 can create

Copy any prompt below and try it yourself in the playground.

Code Refactor

“Refactor this Python function for better efficiency and readability, handling edge cases: def process_data(data): return [x*2 for x in data if x > 0]”

Math Proof

“Prove that the sum of the first n natural numbers is n(n+1)/2 using mathematical induction, step by step.”

JSON Schema

“Generate a JSON schema for a user profile with fields: name, email, age, preferences as array of strings.”

Algorithm Design

“Design an O(n log n) algorithm to find the median of two sorted arrays, provide pseudocode and complexity analysis.”

For Developers

A few lines of code.
V3 inference. Few lines.

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

  • Serverless: scales to zero, scales to millions
  • Pay per token, no minimums
  • Python and JavaScript SDKs, plus REST API
API Documentation 
import requests


response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

FAQ

Common questions about DeepSeek: DeepSeek V3

Read the docs

DeepSeek V3 is a 671B MoE LLM with 37B active parameters per token. It uses MLA and DeepSeekMoE for efficient inference. Trained on 14.8T tokens.

DeepSeek V3 API achieves 60 tokens/second, 3x faster than V2. Supports multi-token prediction for speed gains. Fully compatible with prior APIs.

Yes, DeepSeek V3 is fully open-source with models and papers on GitHub. Outperforms many open models on benchmarks. API access available.

Offers top performance matching closed models at lower cost with 2.8M H800 GPU hours training. Reduces memory by 50% via eight-bit precision. Stable training.

DeepSeek V3 supports function calling and structured outputs via endpoints. Handles text and documents input/output. Batch predictions enabled.

Integrate via standard LLM endpoints with API key. Send prompts for reasoning, coding, or agent tasks. Check docs for MLA and MoE usage.

Ready to create?

Start generating with DeepSeek: DeepSeek V3 on ModelsLab.

Try DeepSeek: DeepSeek V3API Documentation