Nex-N2-Pro
Nex AGI • text
Nex-N2-Pro is an agentic mixture-of-experts model from Nex AGI, with 17B active parameters out of 397B total. Built on the Qwen3.5 architecture, it accepts text and image input and produces...
Specifications
Context Window
262K tokens
Input Price/1M
$0.50
Output Price/1M
$2.50
Parameters
—
Speed
86 tok/s
Latency (TTFT)
988ms
Max Output
262K tokens
Benchmarks
Nex-N2-Pro results on the main AI model evaluation benchmarks. Higher scores indicate better performance.
Coding
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA Coding Index | 59.1 | 100.0 | Artificial Analysis official API |
| SciCode | 42.0 | 100.0 | — |
Long Context
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA-LCR | 68.0 | 100.0 | — |
overall
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA Intelligence Index | 41.0 | 100.0 | Artificial Analysis official API |
Reasoning
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| GPQA Diamond | 89.0 | 100.0 | Artificial Analysis official API |
| IFBench | 66.0 | 100.0 | — |
| HLE | 32.0 | 100.0 | — |
Tool Use
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Tau²-Bench | 82.0 | 100.0 | — |
Information
- Release date
- June 02, 2026
- Tool Calling
- ❌ Not supported
- Vision
- ✅ Supported
- Audio
- ❌ Not supported
Full Analysis: Nex-N2-Pro
What is Nex-N2-Pro?
Nex-N2-Pro is an AI model developed by Nex AGI, classified as a text model. It focuses on text processing and natural language generation. As a proprietary model, it is available via Nex AGI's cloud API. With a context window of 262K tokens, it is suitable for processing long documents such as contracts, books, and complete codebases.
Pricing & Costs in 2026
Nex-N2-Pro is usage-based, priced at $0.5/1M input tokens and $2.5/1M 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
Nex-N2-Pro was evaluated on 8 different benchmarks, covering categories like Coding, Long Context, overall, Reasoning, Tool Use. Results show solid performance across available evaluations.
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-N2-Pro specializes in text, offering advanced capabilities for creating and processing text content.
Comparison with Alternatives
In the 2026 AI model ecosystem, Nex-N2-Pro 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-N2-Pro?
Nex-N2-Pro is an AI model developed by Nex AGI. It is a text model.
How much does Nex-N2-Pro cost?
Nex-N2-Pro costs $0.5/1M input tokens and $2.5/1M 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-N2-Pro compare with other models?
In available benchmarks, Nex-N2-Pro scored: AA Coding Index: 59.1/100, SciCode: 42/100, AA-LCR: 68/100. See the full table above for a detailed comparison.
Is Nex-N2-Pro open source?
No, Nex-N2-Pro is a proprietary model from Nex AGI. It is available via cloud API. For open source alternatives, check our open source model ranking.
What is Nex-N2-Pro best for?
Nex-N2-Pro excels at multimodal tasks including text and vision. With its large context window, it handles long documents, codebases, and extended conversations.
Last updated: June 28, 2026 • View methodology →