---
title: DeepSeek V3 — Open MoE LLM | ModelsLab
description: Access DeepSeek V3 671B MoE model via API for fast inference at 60 tokens/second. Generate superior reasoning outputs now.
url: https://modelslab.com/deepseek-deepseek-v3
canonical: https://modelslab.com/deepseek-deepseek-v3
type: website
component: Seo/ModelPage
generated_at: 2026-05-05T20:03:17.293073Z
---

Available now on ModelsLab · Language Model

DeepSeek: DeepSeek V3
Scale Reasoning Efficiently
---

[Try DeepSeek: DeepSeek V3](/models/open_router/deepseek-deepseek-chat) [API Documentation](https://docs.modelslab.com)

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](/models/open_router/deepseek-deepseek-chat).

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 ](https://docs.modelslab.com)

PythonJavaScriptcURL

Copy

```
<code>import requests

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

FAQ

Common questions about DeepSeek: DeepSeek V3
---

[Read the docs ](https://docs.modelslab.com)

### What is DeepSeek: DeepSeek V3?

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.

### How fast is deepseek deepseek v3 API?

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

### Is DeepSeek: DeepSeek V3 model open source?

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

### What makes DeepSeek: DeepSeek V3 alternative better?

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.

### Does DeepSeek: DeepSeek V3 LLM support function calling?

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

### How to use deepseek deepseek v3 api?

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?
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Start generating with DeepSeek: DeepSeek V3 on ModelsLab.

[Try DeepSeek: DeepSeek V3](/models/open_router/deepseek-deepseek-chat) [API Documentation](https://docs.modelslab.com)

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*This markdown version is optimized for AI agents and LLMs.*

**Links:**
- [Website](https://modelslab.com)
- [API Documentation](https://docs.modelslab.com)
- [Blog](https://modelslab.com/blog)

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*Generated by ModelsLab - 2026-05-06*