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Available now on ModelsLab · Language Model

DeepSeek: DeepSeek V3.2Reason Fast. Scale Agents

Try DeepSeek: DeepSeek V3.2API Documentation

Master Efficiency. Dominate Reasoning

Sparse Attention

DeepSeek Sparse Attention

DSA cuts compute in long-context tasks without quality loss.

Agent Training

85k+ Agent Tasks

Synthesized data from 1800 environments boosts tool-use and generalization.

RL Scaling

GPT-5 Level Performance

Post-training compute rivals closed models in reasoning and agents.

Examples

See what DeepSeek: DeepSeek V3.2 can create

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

Code Optimizer

“Analyze this Python function for efficiency issues and rewrite it using vectorized NumPy operations while preserving exact output.”

Math Proof

“Prove that for any prime p > 3, p^2 - 1 is divisible by 24 using modular arithmetic step by step.”

Agent Plan

“Plan a multi-step workflow to research market trends for electric vehicles, including web search simulation, data aggregation, and summary report.”

Long Context Summary

“Summarize key arguments from this 50k-token research paper on sparse attention mechanisms, highlighting innovations and benchmarks.”

For Developers

A few lines of code.
Reasoning agents. One call.

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 V3.2

Read the docs

DeepSeek V3.2 is an open MoE LLM with 671B parameters balancing efficiency and reasoning. It introduces DSA for long contexts. Matches GPT-5 in agent tasks.

Available via API with text inputs and outputs up to 65k tokens. Supports function calling and structured outputs. Context length reaches 163k tokens.

DeepSeek Sparse Attention reduces compute in training and inference. Uses MLA from prior versions. Post-training scales to 10% of pre-training compute.

DeepSeek V3.2 hits GPT-5 performance in reasoning and agents. V3.2-Speciale rivals Gemini-3.0-Pro with IMO gold medals. Cost-efficient open option.

Trained on 85k tasks across 1800 environments for tool-use. Supports thinking in tool-use modes. Improves instruction-following in interactive setups.

Live on API with 50%+ price cuts from experimental version. Global availability with dynamic quotas. Check docs for exact rates and regions.

Ready to create?

Start generating with DeepSeek: DeepSeek V3.2 on ModelsLab.

Try DeepSeek: DeepSeek V3.2API Documentation