Laguna S 2.1
poolside · released Jul 13, 2026 · poolside/Laguna-S-2.1
- Type
- Open weightsopenmdw-1.1
- Params
- 118B
- Context
- 1M
active per word not recorded by us · about 786K words of context · download allowed, licence restricts use
Our take
Written Sep 17, 2026Laguna S 2.1 is a text model you can download and run yourself, or reach through a host, and its licence puts conditions on commercial use and redistribution. Nothing here measures how good its answers are, so the decision rests on the licence and on a trial of your own.
Use it for text-only work where you can judge the output yourself and the licence terms are acceptable, or when you want the option to run the model on your own hardware. Its request capacity takes long documents without splitting them up first, though reliable recall across all of it is unverified. Skip it if you need measured evidence of coding, reasoning or long-document recall before committing.
The case for it
- Long documents go in without being split up first, though room to hold them is not a guarantee of accurate recall.
- The hosts we track list it at a low rate, so it is worth pricing against your own volume before you commit.
The case against it
- No benchmark scores are supplied, so nothing here says how well it codes, reasons or recalls long documents; trial it on work you can check yourself.
- The licence puts conditions on commercial use and redistribution, so it needs reading before you build on it (OpenMDW 1.1).
How good is it?
We hold no score for this model.
So there is no figure here for everyday use, coding, agent work or writing. That is a gap in our data, not a low score.
Can you run it yourself?
GeForce RTX 4090 · 24 GB
Too large for this card. The weights do not fit even with part of them offloaded to system memory.
Apple M1 Pro (16-core GPU) · 32 GB
Too large for this card. The weights do not fit even with part of them offloaded to system memory.
Comfortable fit
Apple M2 Ultra (76-core GPU) · 192 GB
Room to spare. 43.5 GB spare means a 10% error in the size would not change the answer.
Memory use by level
Against a 24 GB card.
What is quantisation? →This model on every device we track71 devicesThe Q4 build most people download, on each device: what the weights come to, how much context the memory leaves, and whether it runs. Smallest device that runs it first.
Check against your own machine → · Where to rent it hosted →
Or rent it from someone else
Prices checked 2 hours ago — each listing carries its own date.
- per 1M tokens
- $0.090 in / $0.18 out
- Context served
- 1M
- Throughput
- Not measured
| Provider | In / out per 1M tokens | Context | Throughput | Trains on prompts | Logs prompts | Zero retention |
|---|---|---|---|---|---|---|
| OpenRouterOpenRouter's own listing | $0.090 / $0.18checked 2 hours ago | 1M | not measured | Unknown | Unknown | Unknown |
| Poolsidefp4Through OpenRouter | $0.090 / $0.18checked 2 hours ago | 1M131K max reply | 70 tok/s | No | Yesunknown period | Unknown |
Across the 2 listings we hold: 1 says it does not train on prompts, 0 say they do and 1 does not say. 0 appear in the zero-retention registry we check; the rest are unknown to us.
What each host's API supports
From the parameter list each endpoint publishes. Streaming is omitted: nothing we hold reports it, for any model.
| Provider | Tool calling | JSON output | Strict schema |
|---|---|---|---|
| OpenRouterOpenRouter's own listing | ✓ | ✗ | ✗ |
| Poolsidefp4Through OpenRouter | ✓ | ✗ | ✗ |
Tool calling: 2 of 2 listings say yes. JSON output: 0 of 2 listings say yes, 2 say no. Strict schema: 0 of 2 listings say yes, 2 say no.
When we formed this view
Recent changes
What moved
first indexed by our pipelineEach date is the day we first saw the change, or the day the maker announced it.
What we do not know about this model yet
- No independent board has scored it, so we hold no quality figures at all.
- We do not hold the active parameter count for it, so how much of it runs on any one token is unknown to us.
- Nothing we hold says whether an endpoint streams, so we do not show it either way.
- 1 of 2 listings does not say whether it trains on prompts.
- We hold no batch or off-peak rate for any of its listings.
- We hold a decode speed for it, but no prompt-processing (prefill) figure, so how long the input side of a job takes is unknown to us.
Licence and identifiers
What the licence allowsopenmdw-1.1, what it allows commercially, and the identifiers you need to pull this model — its Hugging Face repo, our slug and a machine-readable card.
Licence
openmdw-1.1
License tag "openmdw-1.1" imported from Hugging Face; terms pending curation.
Identifiers
- Hugging Face
- poolside/Laguna-S-2.1
- Architecture
- Mixture of experts
- Takes in, gives back
- Text in, text out
- Catalogue slug
- poolside-laguna-s-2-1