Rank #32
Nex AGI: Nex-N2.5-Mini (free)
- Multimodal
- 262k ctx
- 1M
- $0.00
Model · Vendors
Nex AGI
Ranked models from this vendor or team on Global rankings (same data snapshot as the main global board).
Board snapshot:
About this vendor
Nex AGI lists packaged variants and partner routes on OpenRouter catalogs. Use this page to inspect ranked Nex AGI SKUs and follow links to per-model snapshot metrics.
Vendor snapshot
Catalog models 4
Ranked on Global board 1
Best rank #32
Price range (avg/1M) $0.06–$0.16/1M
Max context 262k ctx
Model types
Origins
Top ranked models
Models on this board · Global rankings
| Rank | Name | Type | Key metric | 1M tokens (avg) |
|---|---|---|---|---|
| 32 | Nex AGI: Nex-N2.5-Mini (free) | Multimodal | 262k ctx | $0.00 |
Catalog models (not in ranking)
| Rank | Name | Size | Price | Notes |
|---|---|---|---|---|
| — | Nex AGI: Nex-N2.5-Mini | 262k ctx | $0.06/1M | nex-agi/nex-n2.5-mini |
| — | Nex AGI: Nex-N2.5-Mini (free) | 262k ctx | $0.00/1M | nex-agi/nex-n2.5-mini:free |
| — | Nex AGI: Nex-N2.5-Pro | 262k ctx | $0.16/1M | nex-agi/nex-n2.5-pro |
| — | Nex AGI: Nex-N2.5-Pro (free) | 262k ctx | $0.00/1M | nex-agi/nex-n2.5-pro:free |
Data sources & methodology
Counts and prices come from the OpenRouter catalog snapshot merged with AI Hippo Global rankings (same snapshot as /rankings/). Rank reflects the composite score on that board—not LMSYS Chatbot Arena ELO. Snapshot date: 2026-09-23. Methodology
FAQ
What is Nex AGI's highest-ranked model?
Nex AGI: Nex-N2.5-Mini (free) currently ranks #32 on AI Hippo's Global rankings board (snapshot 2026-09-23).
What does Nex AGI charge per 1M tokens?
Across catalog and ranked rows, blended 1M-token prices range from $0.06 to $0.16 in the latest snapshot (2026-09-23). Check individual model pages for prompt vs completion splits.
How many Nex AGI models are ranked vs catalog-only?
1 of 4 catalog models appear on Global rankings; the remaining 3 are listed under catalog-only on this page.
How is the Global rankings score calculated?
Global rankings uses on-site weighting (context window first, then blended 1M-token price)—not third-party ELO. Full methodology: /en/methodology/