LLMnex-agi

Nex AGI: Nex-N2-Pro

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...

Anyone in the Space can @-mention Nex AGI: Nex-N2-Pro with the team's shared context - pooled credits, one chat, one memory.

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Starter is free forever - 1 Space, 100 credits/month, 1 MCP. No card.

Verdict

Nex-N2-Pro offers a 262K context window at aggressive pricing — $0.50 input makes it one of the cheapest options for long-document work. Without public benchmarks, you're trading proven performance data for cost savings and multimodal capability. Reach for this when budget constraints matter more than established track records, especially for vision-plus-text workflows where you can validate output quality yourself.

Best for

Budget-conscious long-context processing
Multimodal tasks with cost constraints
Internal workflows where you validate output
Exploratory projects with tight budgets

Strengths

The pricing structure undercuts most competitors by 50-70% on input tokens while maintaining a 262K context window — useful for processing entire codebases or long documents without chunking. Multimodal support at this price point is rare, making it viable for screenshot analysis or document OCR tasks where you can afford to spot-check results. The output pricing at $2.50/Mtok stays competitive for generation-heavy workloads.

Trade-offs

No public benchmarks means you're flying blind on accuracy, reasoning depth, and instruction-following compared to established models like GPT-4o or Claude. Without MMLU, HumanEval, or GPQA scores, you can't predict performance on complex reasoning or coding tasks. The lack of third-party validation makes this a risky choice for production systems where errors carry real cost. You'll need to build your own eval suite before trusting it with critical work.

Specifications

Provider: nex-agi
Category: llm
Context length: 262,144 tokens
Max output: 262,144 tokens
Modalities: text, image
License: proprietary
Released: 2026-06-08

Pricing

Input: $0.50/Mtok
Output: $2.50/Mtok
Model ID: nex-agi/nex-n2-pro

Per-token prices show what the model costs upstream. On Switchy your team draws from one shared org credit pool - one plan, one balance for everyone.

Team cost calculator

Seats5 peopleMessages / seat / day80Avg turn size2 ktokOutput share30 %

Estimated monthly spend

$19.36

17.6M tokens / month
5 seats · 80 msgs/day

Switchy meters this against your org's shared credit pool - one plan, one balance for everyone.

Providers

Provider	Context	Input	Output	P50 latency	Throughput	30d uptime
nex-agi	262k	$0.50/Mtok	$2.50/Mtok	—	—	—

Performance

Performance snapshots are collected daily. Check back after the next ingestion run.

Benchmarks

Public benchmark scores are not available yet for this model. Check back after the next ingestion run.

Works well with

Top MCPs

Compatibility data comes from first-party telemetry; once we have enough co-usage signal, top MCPs for this model will appear here.

How Switchy teams use it

Not enough Spaces have used this model yet to share anonymised team stats. We wait for at least 50 distinct Spaces per week before publishing any aggregate.

Starter prompts

Long Document Summary

Summarize this entire document in 300 words, highlighting the three most important findings and any contradictions between sections. Focus on actionable insights.

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Screenshot to Code

Convert this UI screenshot into semantic HTML and Tailwind CSS. Preserve the layout, spacing, and color scheme exactly as shown. Include accessibility attributes.

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Codebase Q&A

Given this full codebase, explain how the authentication flow works from login to session management. Cite specific file names and line ranges in your answer.

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Multi-Document Comparison

Compare these three policy documents and create a table showing where they agree, disagree, and leave gaps. Note any conflicting definitions or requirements.

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Image Data Extraction

Extract all text, tables, and numerical data from this scanned invoice. Return as structured JSON with fields for vendor, line items, totals, and dates.

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