Rank #27
Ling-3.0-flash (free)
- LLM
- 262k ctx
- 1M
- $0.00
Model · Vendors
Ling-3.0-flash
Ranked models from this vendor or team on Global rankings (same data snapshot as the main global board).
Board snapshot:
About this vendor
Inclusion AI lists multilingual and inclusion-focused models on OpenRouter catalogs. Compare Inclusion AI ranks, context sizes, and snapshot prices with other regional language vendors.
Vendor snapshot
Catalog models 4
Ranked on Global board 1
Best rank #27
Price range (avg/1M) $0.02–$0.35/1M
Max context 262k ctx
Model types
Origins
Top ranked models
Models on this board · Global rankings
| Rank | Name | Type | Key metric | 1M tokens (avg) |
|---|---|---|---|---|
| 27 | Ling-3.0-flash (free) | LLM | 262k ctx | $0.00 |
Catalog models (not in ranking)
| Rank | Name | Size | Price | Notes |
|---|---|---|---|---|
| — | inclusionAI: Ling-2.6-1T | 262k ctx | $0.35/1M | inclusionai/ling-2.6-1t |
| — | inclusionAI: Ling-2.6-flash | 262k ctx | $0.02/1M | inclusionai/ling-2.6-flash |
| — | inclusionAI: Ring-2.6-1T | 262k ctx | $0.35/1M | inclusionai/ring-2.6-1t |
| — | Ling-3.0-flash (free) | 262k ctx | $0.00/1M | inclusionai/ling-3.0-flash: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: 2026-08-02. Methodology
FAQ
What is Ling-3.0-flash's highest-ranked model?
Ling-3.0-flash (free) currently ranks #27 on AI Hippo's Global rankings board (snapshot 2026-08-02).
What does Ling-3.0-flash charge per 1M tokens?
Across catalog and ranked rows, blended 1M-token prices range from $0.02 to $0.35 in the latest snapshot (2026-08-02). Check individual model pages for prompt vs completion splits.
How many Ling-3.0-flash 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/