Model decision surface
Compare AI models
Start with Gemma 4 26B A4B and Laguna S 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
Gemma 4 26B A4B vs Laguna S 2.1
Live dataset updated 9/10/2026, 1:00:55 AM UTC
Open 1200×630 evidence receipt| Field | Gemma 4 26B A4B | Laguna S 2.1 Poolside |
|---|---|---|
| ModelCap position | #38 | #39 |
| Index score | 65.0 | 64.4 |
| Evidence | Measured2 public benchmark observations across 2 boards | Estimated5 reported rows against measured corpus ladders (70%); global-corpus-prior over 183 held-out anchors (30%); shrunk 4.6 toward the measured corpus |
| Input / 1M | $0.07 | $0.09 |
| Output / 1M | $0.34 | $0.18 |
| Pricing status | fresh | fresh |
| Context | 262K | 1M |
| Providers | 9 | 1 |
| Weight access | Open weights | Restricted license |
Decision facts
- Gemma 4 26B A4B is #38; Laguna S 2.1 is #39 on the same current language board.
- Index scores are 65.0 for Gemma 4 26B A4B and 64.4 for Laguna S 2.1. Their published uncertainty intervals overlap, so the rank difference alone does not establish a reliable capability advantage for your workload.
- Evidence differs: Gemma 4 26B A4B is Measured; Laguna S 2.1 is Estimated.
- Listed output price per 1M tokens is $0.34 for Gemma 4 26B A4B and $0.18 for Laguna S 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 $0.31 for Gemma 4 26B A4B versus $0.27 for Laguna S 2.1. Laguna S 2.1 costs 12.9% 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 Gemma 4 26B A4B and 1,048,576 for Laguna S 2.1.
- Weight access differs: Gemma 4 26B A4B is open; Laguna S 2.1 is restricted.
- ModelCap currently lists 9 providers for Gemma 4 26B A4B and 1 for Laguna S 2.1.
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.