Model decision surface
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Start with Ling-2.6-1T and MiniMax M2-her, 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-2.6-1T vs MiniMax M2-her
Live dataset updated 8/18/2026, 3:36:20 AM UTC
Open 1200×630 evidence receipt| Field | Ling-2.6-1T InclusionAI | MiniMax M2-her MiniMax |
|---|---|---|
| ModelCap position | #74 | #89 |
| Index score | 36.0 | 27.5 |
| Evidence | Measured1 public benchmark observation across 1 board | Estimatedpublisher corpus prior · leave-one-anchor-out calibrated |
| Input / 1M | $0.075 | $0.30 |
| Output / 1M | $0.625 | $1.20 |
| Pricing status | fresh | fresh |
| Context | 262K | 66K |
| Providers | 1 | 1 |
| Weight access | API only | API only |
Decision facts
- Ling-2.6-1T is #74; MiniMax M2-her is #89 on the same current language board.
- Index scores are 36.0 for Ling-2.6-1T and 27.5 for MiniMax M2-her.
- Evidence differs: Ling-2.6-1T is Measured; MiniMax M2-her is Estimated.
- Listed output price per 1M tokens is $0.63 for Ling-2.6-1T and $1.20 for MiniMax M2-her.
- Published context is 262,144 tokens for Ling-2.6-1T and 65,536 for MiniMax M2-her.
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.