Nex-N1
This repository contains the internlm3-8B-Nex-N1 model, part of the Nex-N1 series, introduced in the paper Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction.
Nex is a next-generation, full-stack agentic platform that brings foundation models, synthetic data pipelines, RL training, agent frameworks, and deployment tools together in one unified ecosystem. 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. We are committed to making it easier than ever to build and deploy AI agents by offering researchers and entrepreneurs a high-performance, reliable, and cost-effective "out-of-the-box" agent system.
Highlights
- Full spectrum model matrix: From 8B to 671B parameters, the Nex series covers everything from edge-friendly setups to frontier-scale deployments.
- Agent-focused performance: Demonstrates industry-leading results on programming, tool-use, web-search, and other multi-hop reasoning tasks.
- Production-ready utility: Excels at mini-app development, website authoring, slide creation, and immersive role-play—delivering immediate productivity gains.
- End-to-end control: Developers can build the entire data-to-deployment loop on top of Nex, ensuring sovereignty while keeping costs predictable.
- Open ecosystem: Turnkey synthetic data pipelines, curated datasets, Nex-N1 checkpoints, the NexAU Agent framework, the EaaS MoE inference stack, and NexRL training services are all openly available.
Performance
Nex-N1 is evaluated on six representative agentic benchmarks (general + professional). The model consistently ranks at or near the top across tool-using, web-search, and coding-heavy evaluations, showing strong readiness for real-world agent workflows.
Nex-N1 provides various size models from 8B to 671B for different usage scenarios.
| Model | GAIA2 | τ2-Bench | SWE-bench Verified | Terminal-Bench2 | BaxBench | BFCL v4 |
|---|---|---|---|---|---|---|
| DeepSeek-V3.1-Nex-N1 | 29.5 | 80.2 | 70.6 | 31.8 | 59.7 | 65.3 |
| Qwen3-32B-Nex-N1 | 16.7 | 72.1 | 50.5 | 16.7 | 34.8 | 60.5 |
| Qwen3-30B-A3B-Nex-N1 | 11.3 | 65.3 | 29.7 | 8.3 | 13.6 | 51.9 |
| internlm3-8B-Nex-N1 | 8.6 | 63.0 | 20.3 | - | - | 44.5 |
Nex-N1 demonstrates competitive performance across all evaluation scenarios, showing particularly strong results in practical coding and HTML generation tasks.
Refer to https://huggingface.co/datasets/nex-agi/coding-eval and https://huggingface.co/datasets/nex-agi/html-eval for more details.
Usage
Local Deployment
We recommend sglang for serving Nex-series models locally:
python -m sglang.launch_server --model-path /path/to/your/model
Function Calling
Nex-series models support robust function-calling capabilities. To maximize the function-calling capabilities of the Nex-series models, we modified the tool parser of qwen3_coder, see: https://github.com/sgl-project/sglang/pull/13411. To enable this feature, simply add the --tool-call-parser qwen3_coder flag when launching the server:
python -m sglang.launch_server --model-path /path/to/your/model --tool-call-parser qwen3_coder
Mini Program Development
Nex-N1 is optimized for mini program development. For optimal performance, we recommend using Claude Code configured with both context7 and a search MCP.
claude mcp add --transport http context7 https://mcp.context7.com/mcp --header "CONTEXT7_API_KEY: [CONTEXT7_API_KEY]"
claude mcp add --transport stdio serper-search --env SERPER_API_KEY=[SERPER_API_KEY] -- npx -y serper-search-scrape-mcp-server
Refer to https://github.com/upstash/context7 for more details on setting up context7.