Model decision surface
Compare AI models
Start with Ling-3.0-flash and GPT-5.4 Nano, or choose any two current ranked language models. Compare capability evidence, price, context, provider availability, and weight access without pretending one field decides every use case.
Current public data
Ling-3.0-flash vs GPT-5.4 Nano
Live dataset updated 8/19/2026, 2:38:10 AM UTC
Open 1200×630 evidence receipt| Field | Ling-3.0-flash InclusionAI | GPT-5.4 Nano OpenAI |
|---|---|---|
| ModelCap position | #41 | #44 |
| Index score | 53.0 | 52.6 |
| Evidence | Measured1 public benchmark observation across 1 board | Measured4 public benchmark observations across 4 boards |
| Input / 1M | $0.021 | $0.20 |
| Output / 1M | $0.063 | $1.25 |
| Pricing status | fresh | fresh |
| Context | 262K | 400K |
| Providers | 2 | 2 |
| Weight access | Open weights | API only |
Decision facts
- Ling-3.0-flash is #41; GPT-5.4 Nano is #44 on the same current language board.
- Index scores are 53.0 for Ling-3.0-flash and 52.6 for GPT-5.4 Nano.
- Both positions use Measured evidence.
- Listed output price per 1M tokens is $0.06 for Ling-3.0-flash and $1.25 for GPT-5.4 Nano.
- Published context is 262,144 tokens for Ling-3.0-flash and 400,000 for GPT-5.4 Nano.
- Weight access differs: Ling-3.0-flash is open; GPT-5.4 Nano is none.
These are separate published fields, not a synthetic winner. ModelCap does not collapse price, access, context, and capability evidence into a hidden recommendation score.
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Each comparison page is a permanent, shareable URL with the same live figures as this tool: ModelCap Index position, API pricing, context window, provider count, weight access and every shared benchmark board.