Model decision surface
Compare AI models
Start with Mistral Medium 3.5 and Laguna XS 2.1, 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
Mistral Medium 3.5 vs Laguna XS 2.1
Live dataset updated 9/15/2026, 9:19:37 PM UTC
Open 1200×630 evidence receipt| Field | Mistral Medium 3.5 Mistral AI | Laguna XS 2.1 Poolside |
|---|---|---|
| ModelCap position | #63 | #81 |
| Index score | 55.9 | 50.8 |
| Evidence | Measured4 public benchmark observations across 4 boards | Estimated3 reported rows against measured corpus ladders (27%); global-corpus-prior over 194 held-out anchors (73%); shrunk 3.7 up toward the measured corpus |
| Input / 1M | $1.50 | $0.06 |
| Output / 1M | $7.50 | $0.12 |
| Pricing status | fresh | fresh |
| Context | 262K | 262K |
| Providers | 1 | 1 |
| Weight access | API only | Restricted license |
Decision facts
- Mistral Medium 3.5 is #63; Laguna XS 2.1 is #81 on the same current language board.
- Index scores are 55.9 for Mistral Medium 3.5 and 50.8 for Laguna XS 2.1. Their published uncertainty intervals overlap, so the rank difference alone does not establish a reliable capability advantage for your workload.
- Evidence differs: Mistral Medium 3.5 is Measured; Laguna XS 2.1 is Estimated.
- Listed output price per 1M tokens is $7.50 for Mistral Medium 3.5 and $0.12 for Laguna XS 2.1. For 1,000 requests with 2,000 input and 500 output tokens each (2M input + 0.5M output), the listed-rate estimate is $6.75 for Mistral Medium 3.5 versus $0.18 for Laguna XS 2.1. Laguna XS 2.1 costs 97.3% 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 Mistral Medium 3.5 and 262,144 for Laguna XS 2.1.
- Weight access differs: Mistral Medium 3.5 is none; Laguna XS 2.1 is restricted.
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.