Rank #10
Poolside: Laguna S 2.1
- LLM
- 1.0M ctx
- 1M
- $0.14
Model · Vendors
Poolside
Ranked models from this vendor or team on Global rankings (same data snapshot as the main global board).
Board snapshot:
About this vendor
Poolside builds code-native foundation models aimed at software engineering automation. Track Poolside ranks on Global Rankings and evaluate snapshot pricing relative to Codestral, GPT, and DeepSeek code SKUs.
Vendor snapshot
Catalog models 4
Ranked on Global board 1
Best rank #10
Price range (avg/1M) $0.09–$0.14/1M
Max context 1.0M ctx
Model types
Origins
Top ranked models
Models on this board · Global rankings
| Rank | Name | Type | Key metric | 1M tokens (avg) |
|---|---|---|---|---|
| 10 | Poolside: Laguna S 2.1 | LLM | 1.0M ctx | $0.14 |
Catalog models (not in ranking)
| Rank | Name | Size | Price | Notes |
|---|---|---|---|---|
| — | Poolside: Laguna S 2.1 | 1.0M ctx | $0.14/1M | poolside/laguna-s-2.1 |
| — | Poolside: Laguna S 2.1 (free) | 262k ctx | $0.00/1M | poolside/laguna-s-2.1:free |
| — | Poolside: Laguna XS 2.1 | 262k ctx | $0.09/1M | poolside/laguna-xs-2.1 |
| — | Poolside: Laguna XS 2.1 (free) | 262k ctx | $0.00/1M | poolside/laguna-xs-2.1:free |
Data sources & methodology
Counts and prices come from the OpenRouter catalog snapshot merged with AI Hippo Global rankings (same snapshot as /rankings/). Rank reflects the composite score on that board—not LMSYS Chatbot Arena ELO. Snapshot date: 2026-08-02. Methodology
FAQ
What is Poolside's highest-ranked model?
Poolside: Laguna S 2.1 currently ranks #10 on AI Hippo's Global rankings board (snapshot 2026-08-02).
What does Poolside charge per 1M tokens?
Across catalog and ranked rows, blended 1M-token prices range from $0.09 to $0.14 in the latest snapshot (2026-08-02). Check individual model pages for prompt vs completion splits.
How many Poolside models are ranked vs catalog-only?
1 of 4 catalog models appear on Global rankings; the remaining 3 are listed under catalog-only on this page.
How is the Global rankings score calculated?
Global rankings uses on-site weighting (context window first, then blended 1M-token price)—not third-party ELO. Full methodology: /en/methodology/