Model decision surface
Compare AI models
Start with Laguna XS 2.1 and GLM 5 Turbo, 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
Laguna XS 2.1 vs GLM 5 Turbo
Live dataset updated 9/15/2026, 5:33:33 AM UTC
Open 1200×630 evidence receipt| Field | Laguna XS 2.1 Poolside | GLM 5 Turbo Z.ai |
|---|---|---|
| ModelCap position | #77 | #80 |
| Index score | 50.4 | 50.0 |
| Evidence | Estimated3 reported rows against measured corpus ladders (29%); global-corpus-prior over 187 held-out anchors (71%); shrunk 3.6 up toward the measured corpus | Measuredpublisher-corpus-prior over 187 held-out anchors (65%); 1 specialist-board observation at 1.3% support (35%); shrunk 23.3 up toward the measured corpus |
| Input / 1M | $0.06 | $1.20 |
| Output / 1M | $0.12 | $4.00 |
| Pricing status | fresh | fresh |
| Context | 262K | 203K |
| Providers | 1 | 1 |
| Weight access | Restricted license | API only |
Decision facts
- Laguna XS 2.1 is #77; GLM 5 Turbo is #80 on the same current language board.
- Index scores are 50.4 for Laguna XS 2.1 and 50.0 for GLM 5 Turbo. Their published uncertainty intervals overlap, so the rank difference alone does not establish a reliable capability advantage for your workload.
- Evidence differs: Laguna XS 2.1 is Estimated; GLM 5 Turbo is Measured.
- Listed output price per 1M tokens is $0.12 for Laguna XS 2.1 and $4.00 for GLM 5 Turbo. For 1,000 requests with 2,000 input and 500 output tokens each (2M input + 0.5M output), the listed-rate estimate is $0.18 for Laguna XS 2.1 versus $4.40 for GLM 5 Turbo. Laguna XS 2.1 costs 95.9% less in this scenario. This excludes caching, batch discounts, prompt-length tiers, tool charges and retries; verify the selected endpoint before budgeting.
- Published context is 262,144 tokens for Laguna XS 2.1 and 202,752 for GLM 5 Turbo.
- Weight access differs: Laguna XS 2.1 is restricted; GLM 5 Turbo 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.