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Available now on ModelsLab · Language Model

DeepSeek V3-0324Reasoning. Speed. Scale.

Try DeepSeek V3-0324API Documentation

Enterprise-Grade Performance. Open Source.

Massive Context

128K Token Window

Process long documents, conversations, and retrieval tasks in single queries without context loss.

Intelligent Scaling

Multi-Token Prediction

Predict multiple future tokens simultaneously for faster inference and improved accuracy over autoregressive models.

Efficient Architecture

Mixture of Experts

37B activated parameters per token reduce memory overhead while maintaining 685B total capacity for complex reasoning.

Examples

See what DeepSeek V3-0324 can create

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

Math Problem Solving

“Solve this calculus problem step by step: Find the derivative of f(x) = 3x^4 - 2x^2 + 5x - 7 and evaluate at x = 2. Show all work.”

Code Generation

“Write a Python function that implements a binary search algorithm. Include docstring, type hints, and handle edge cases.”

Document Analysis

“Analyze this 50-page technical specification and summarize the key requirements, constraints, and implementation recommendations.”

Multi-Turn Reasoning

“I have a dataset with missing values. First, explain three imputation strategies. Then, recommend which works best for time-series data and why.”

For Developers

A few lines of code.
Reasoning LLM. Three 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 V3-0324

Read the docs

Multi-token prediction allows the model to predict multiple future tokens simultaneously, overcoming autoregressive bottlenecks. It achieves 20 tokens per second on standard hardware, making it ideal for real-time applications.

The expanded context enables processing of long documents, multi-turn conversations, and retrieval-augmented generation without truncation. This is critical for document analysis and knowledge-intensive tasks.

V3-0324 shows significant benchmark gains: MMLU-Pro +5.3, GPQA +9.3, and AIME +19.8 points over the base V3. Enhanced post-training draws from reasoning techniques, improving logic and problem-solving capabilities.

Yes. With 685B parameters and Mixture-of-Experts architecture, it's designed for cost-effective inference at scale. It outperforms many closed-source models while maintaining lower computational overhead than dense alternatives.

Only 37B of 685B parameters activate per token, dramatically reducing memory and compute requirements during inference. This sparse activation keeps costs low while maintaining performance comparable to much larger models.

Coding assistance, mathematical reasoning, long-form content generation, tool calling, and agentic workflows. It's particularly strong in tasks requiring both creativity and structured problem-solving.

Ready to create?

Start generating with DeepSeek V3-0324 on ModelsLab.

Try DeepSeek V3-0324API Documentation