Nex AGI: DeepSeek V3.1 Nex N1

Nex AGI: DeepSeek V3.1 Nex N1

Nex AGILLM

DeepSeek V3.1 Nex-N1 is the flagship release of the Nex-N1 series — a post-trained model designed to highlight agent autonomy, tool use, and real-world productivity. Nex-N1 demonstrates competitive performance across...

Open SourceAPI AvailableTool Calling

Specifications

Context Window

131K tokens

Input Price/1M

$0.14

Output Price/1M

$0.50

Parameters

Max Output

164K tokens

Information

Tool Calling
✅ Supported
Vision
❌ Not supported
Audio
❌ Not supported

Full Analysis: Nex AGI: DeepSeek V3.1 Nex N1

What is Nex AGI: DeepSeek V3.1 Nex N1?

Nex AGI: DeepSeek V3.1 Nex N1 is an AI model developed by Nex AGI, classified as a large language model (LLM). It focuses on text processing and natural language generation. As an open source model, it is available for download, customization, and on-premises deployment. With a context window of 131K tokens, it is suitable for processing long documents such as contracts, books, and complete codebases.

Pricing & Costs in 2026

Nex AGI: DeepSeek V3.1 Nex N1 is usage-based, priced at $0.135/1M input tokens and $0.5/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. At this aggressive price point, it is one of the most cost-effective options on the market, ideal for high-volume applications like chatbots, bulk document analysis, and automation.

Benchmarks & Performance

We don't have detailed benchmark results for Nex AGI: DeepSeek V3.1 Nex N1 yet. Benchmarks are updated weekly as new data becomes available from sources like Artificial Analysis, LM Arena, and LiveBench.

It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.

Recommended Use Cases

Nex AGI: DeepSeek V3.1 Nex N1 is suitable for a wide range of AI applications: long document analysis (contracts, legal proceedings, codebases), automation with tool calling (API integration, databases, external systems), high-volume chatbots and automated support, text generation, summarization, translation, and general assistance.

Comparison with Alternatives

In the 2026 AI model ecosystem, Nex AGI: DeepSeek V3.1 Nex N1 competes directly with similarly capable models. Nex AGI competes in this segment against OpenAI, Anthropic, Google, and Meta. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.

For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.

Frequently Asked Questions

What is Nex AGI: DeepSeek V3.1 Nex N1?

DeepSeek V3.1 Nex-N1 is the flagship release of the Nex-N1 series — a post-trained model designed to highlight agent autonomy, tool use, and real-world productivity. Nex-N1 demonstrates competitive performance across...

How much does Nex AGI: DeepSeek V3.1 Nex N1 cost?

Nex AGI: DeepSeek V3.1 Nex N1 costs $0.135/1M input tokens and $0.5/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.

How does Nex AGI: DeepSeek V3.1 Nex N1 compare with other models?

We don't have detailed benchmarks for Nex AGI: DeepSeek V3.1 Nex N1 yet. Check the main benchmark page to compare available models.

Is Nex AGI: DeepSeek V3.1 Nex N1 open source?

Yes, Nex AGI: DeepSeek V3.1 Nex N1 is an open source model. You can deploy it on-premises, customize it via fine-tuning, and maintain full control over your data. Check the official repository for the specific license.

What is Nex AGI: DeepSeek V3.1 Nex N1 best for?

Nex AGI: DeepSeek V3.1 Nex N1 excels at general-purpose language tasks. With its large context window, it handles long documents, codebases, and extended conversations. It supports tool calling for API integrations and automation.

Last updated: May 15, 2026 View methodology →