A Chinese AI lab just released a free model that scores 80.8 on SWE-Bench Verified and 75.3 on Terminal-Bench 2.1, putting it in the same league as GPT-5.5 and Claude Opus 4.7 on real coding work. It is called Nex-N2-Pro, it dropped on June 2, 2026, and it costs nothing to download. The model comes from Nex AGI, ships under a permissive Apache 2.0 license, and is built specifically for agentic tasks like writing code, calling tools, and running long, multi-step workflows on its own.
This is one of the clearest signs yet that open-source AI has caught up with the frontier on the tasks developers actually care about. In this article we break down what Nex-N2-Pro is, what its benchmark scores really mean, how it stacks up against GPT-5.5 and Opus 4.7, what it costs, and who should actually bother with it. We also cover the part most launch posts skip, which is the very real hardware needed to run a 397-billion-parameter model on your own machine.
The Key Takeaways
- Nex-N2-Pro is free and open-source under Apache 2.0, released by Nex AGI on June 2, 2026
- It is a 397B-parameter Mixture-of-Experts model that activates only 17B parameters per token, built on Qwen3.5-397B-A17B
- Headline scores include 80.8 SWE-Bench Verified, 75.3 Terminal-Bench 2.1, 90.7 GPQA Diamond, and 94.0 IFEval
- It keeps pace with GPT-5.5 and Claude Opus 4.7 on coding and agentic benchmarks, the first open model to do so this convincingly
- A smaller sibling, Nex-N2-mini, targets lighter hardware, but running the full Pro model locally needs roughly 794 GB of VRAM at full precision
What Is Nex-N2-Pro?
Nex-N2-Pro is an open-source AI model from Nex AGI designed for agentic work rather than simple chat. It uses a Mixture-of-Experts (MoE) design with 397 billion total parameters but activates only 17 billion per token, which keeps inference far cheaper than a dense model of the same size. It accepts both text and images as input and produces text output, and it supports a 262,000-token context window with output up to 256,000 tokens.
The model is post-trained on Qwen3.5-397B-A17B, Alibaba’s open base model, so it inherits that lineage while adding Nex AGI’s own agentic training on top. Nex-N2-Pro is the larger of two releases. Its sibling, Nex-N2-mini, is built on the smaller Qwen3.5-35B-A3B base for people who want something lighter to run. Both are open under Apache 2.0, which means you can use them commercially, modify them, and self-host them without paying a license fee.
If you have followed recent open releases like Alibaba’s Qwen pricing and model lineup or MiniMax M3, Nex-N2-Pro fits the same wave of capable, openly licensed models. The difference is how aggressively it targets agentic coding.
Nex-N2-Pro Benchmarks: How Good Is It Really?
The benchmark sheet is where Nex-N2-Pro earns its attention. On software engineering and agentic tasks, it posts numbers that until recently were the exclusive territory of closed frontier models. The figures below come from the official Hugging Face model card.
| Benchmark | What it measures | Nex-N2-Pro |
|---|---|---|
| SWE-Bench Verified | Real GitHub bug fixes | 80.8 |
| Terminal-Bench 2.1 | Command-line / agent tasks | 75.3 |
| SWE-Bench Pro | Harder software engineering | 58.8 |
| GPQA Diamond | Graduate-level science reasoning | 90.7 |
| IFEval | Instruction following | 94.0 |
| BrowseComp | Web browsing and research | 83.7 |
| GDPval | Long-horizon economic tasks | 1585 |
The standout is 80.8 on SWE-Bench Verified, a test that asks a model to fix real bugs in real open-source repositories. A score in the 80s means the model is useful for day-to-day engineering work, not just demos. The 94.0 on IFEval also matters for agent builders, because an agent that drifts from instructions mid-task is worse than no agent at all.
How Nex-N2-Pro Compares to GPT-5.5 and Opus 4.7
Nex AGI positions the model as a peer to the closed leaders, and the head-to-head numbers it published back that up on coding. Nex-N2-Pro edges GPT-5.5 on SWE-Bench Pro and beats Claude Opus 4.7 on Terminal-Bench 2.1, while trailing GPT-5.5 slightly on web browsing.
| Benchmark | Nex-N2-Pro | GPT-5.5 | Opus 4.7 |
|---|---|---|---|
| Terminal-Bench 2.1 | 75.3 | N/A | 69.7 |
| SWE-Bench Pro | 58.8 | 58.6 | N/A |
| BrowseComp | 83.7 | 84.4 | 79.8 |
The honest read is that Nex-N2-Pro is competitive, not dominant. It trades blows with the best closed models on coding and agentic work, loses by a hair on some browsing tasks, and wins big on one metric that the others cannot match, which is price. You can read our take on the closed competition in our Claude Opus coverage and see where it sits on our best AI models hub.
What Makes “Agentic Thinking” Different
Nex AGI’s pitch centers on a framework it calls Agentic Thinking, which ties together understanding a request, planning, writing and running code, reading the result, and iterating into one continuous loop. Most models treat reasoning, tool use, and execution as separate steps. Nex-N2-Pro is trained to keep them in a single flow, which is closer to how a human engineer actually works through a problem.
Two ideas sit underneath it. Adaptive Thinking lets the model decide on its own when to reason deeply and when to act fast, which Nex AGI claims cuts thinking tokens by 30 to 50 percent on routine steps. Coherent Thinking keeps one consistent reasoning style across different task types, so the model does not behave like a different system when it switches from research to coding. Together they are meant to make long, multi-step jobs more reliable, which is exactly where most agents fall apart today.
Pricing and How to Use Nex-N2-Pro
The headline price is the easy part. Because Nex-N2-Pro is open-source under Apache 2.0, the weights are free to download from Hugging Face and ModelScope. During the launch window in June 2026, several hosted providers also offered it free, including OpenRouter on its free tier and SiliconFlow, which advertised free access for the first two weeks. Standard paid per-token rates had not been publicly posted at the time of writing, so expect that to settle once the launch promotions end.
You have a few ways to actually use it. The fastest is through a hosted API like OpenRouter, SiliconFlow, Novita, or FriendliAI, where you send requests and never touch a GPU. The second is downloading the weights and running them yourself with tools like llama.cpp, Ollama, or LM Studio. The third is to skip the model-juggling entirely, which we come back to below.
The Catch: You Probably Cannot Run It Locally
Here is the reality check most launch coverage glosses over. A 397-billion-parameter model is enormous, and running Nex-N2-Pro at full BF16 precision needs roughly 794 GB of VRAM, which is data-center territory and far beyond any single consumer GPU. Even squeezed down to INT4 quantization, it still needs around 216 GB of VRAM, so a single 24 GB card like an RTX 4090 is nowhere close.
For most people that means the API route is the practical one, and the Nex-N2-mini variant is the better pick if you want something that fits on more modest hardware. If you only want the capability without the infrastructure headache, a multi-model app is the simplest path. Our own Fello AI app gives you Claude, ChatGPT, Gemini, Grok, and DeepSeek through one Mac subscription, so you get frontier-grade output without managing GPUs, quantization, or provider accounts yourself.
Nex-N2-Pro vs the Competition
So where does it land against the field? The table below sums up the trade-offs in plain terms.
| Model | Developer | License | Standout strength | Best for |
|---|---|---|---|---|
| Nex-N2-Pro | Nex AGI | Apache 2.0 (open) | Agentic coding and tool use | Free self-hosting, agent builders |
| GPT-5.5 | OpenAI | Proprietary | All-round quality and ecosystem | General users, polished apps |
| Claude Opus 4.7 | Anthropic | Proprietary | Deep reasoning and code quality | Pro developers, long tasks |
| Qwen3.5-397B | Alibaba | Open weights | Strong multilingual base | Builders who want a base model |
| DeepSeek V4 | DeepSeek | Open weights | Cost-efficient reasoning | Budget agentic and reasoning work |
If you need a free model you can host and shape to your own agent stack, Nex-N2-Pro is now one of the strongest options on the board, alongside open releases like NVIDIA’s Nemotron 3 Ultra. If you just want the best answer with zero setup, a closed model or a multi-model app still wins on convenience.
Conclusion
Nex-N2-Pro is a genuine milestone for open-source AI. A free, Apache 2.0 model that matches GPT-5.5 and Claude Opus 4.7 on coding benchmarks would have sounded far-fetched a year ago, and now you can download it today. The honest caveat is hardware, since running the full model takes serious infrastructure, so the API or the smaller Nex-N2-mini will be the realistic entry point for most readers.
If you build agents or write code and want an open model you control, start with the hosted API on OpenRouter or SiliconFlow and test it on your own tasks before committing to self-hosting. If you would rather skip the setup and switch between top models freely, try Fello AI’s all-in-one app instead.
FAQ
Is Nex-N2-Pro free?
Yes. Nex-N2-Pro is open-source under an Apache 2.0 license, so you can download the weights for free from Hugging Face or ModelScope. Several hosts, including OpenRouter and SiliconFlow, also offered free API access during the June 2026 launch window.
What is Nex-N2-Pro built on?
It is post-trained on Qwen3.5-397B-A17B, Alibaba’s open base model. Nex AGI added its own agentic training on top to create the Agentic Thinking behavior the model is known for.
How does Nex-N2-Pro compare to GPT-5.5?
On coding it is highly competitive. Nex-N2-Pro scores 75.3 on Terminal-Bench 2.1 and 58.8 on SWE-Bench Pro, slightly ahead of GPT-5.5 on some software-engineering tests while trailing a little on web browsing. The big difference is that Nex-N2-Pro is open and free.
Can I run Nex-N2-Pro on my own computer?
Not easily. The full model needs around 794 GB of VRAM at full precision and roughly 216 GB even when quantized to INT4, which is well beyond consumer GPUs. Most people should use a hosted API or the lighter Nex-N2-mini instead.
What is the difference between Nex-N2-Pro and Nex-N2-mini?
Both come from Nex AGI and share the Agentic Thinking design. Nex-N2-Pro is the larger 397B model built on Qwen3.5-397B-A17B, while Nex-N2-mini is built on the smaller Qwen3.5-35B-A3B base for lighter, more affordable deployment.
