Laguna M.1
poolside · released Jun 15, 2026 · poolside/Laguna-M.1
- Type
- Open weightsApache License 2.0
- Params
- 226B
- Context
- 262K
active per word not recorded by us · about 197K words of context
Our take
The case for it
- Both listed hosts charge the same low rate, so the bill does not change with the host you pick.
- The licence allows commercial use, changes and redistribution (Apache License 2.0).
- Its request capacity takes a long document in a single request, so material need not be split up first; reliable recall across all of it is unverified in our data.
The case against it
- The one measured result places it near the bottom of its board: 75th of 95 on Arena Code (WebDev) as of 25 Sep 2026, which records which web-app answer people preferred rather than whether it was correct.
- At 225.8 billion parameters this is a size to plan hardware around, not a download for a machine of your own.
How good is it?
EverydayGeneral questions and everyday reasoning
Not yet scored on Arena Text (overall).
CodingWriting and fixing code on its own
Not yet scored on Arena Coding. It is on Arena Code (WebDev), in 75th of 95 with 1348.
AgenticPlanning, calling tools, staying on task
Not yet scored on Arena Agent.
WritingDrafting and rewriting prose
Not yet scored on Arena Creative Writing.
Every published score for this model1 scoreEvery figure we hold, from 1 board, with who ran it and a link to the source — including the boards no rating above is built on.
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 M3 Ultra (80-core GPU) · 512 GB
Room to spare. 235.6 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 months ago — each listing carries its own date.
- per 1M tokens
- $0.20 in / $0.40 out
- Context served
- 262K
- Throughput
- Not measured
| Provider | In / out per 1M tokens | Context | Throughput | Trains on prompts | Logs prompts | Zero retention |
|---|---|---|---|---|---|---|
| OpenRouterOpenRouter's own listing | $0.20 / $0.40checked 2 months ago | 262K | not measured | Unknown | Unknown | Unknown |
| Poolsidefp4Through OpenRouter | $0.20 / $0.40checked 2 months ago | 262K | not measured | 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: 1 of 2 listings says yes, 1 publishes no parameter list. JSON output: 0 of 2 listings say yes, 1 says no, 1 publishes no parameter list. Strict schema: 0 of 2 listings say yes, 1 says no, 1 publishes no parameter list.
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
- We hold no measured file for it, so all 3 sizes on this page are calculated from the parameter count.
- 1 of 2 listings publishes no parameter list, so what its API accepts is unknown to us.
- 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 cached-input rate for any of its listings.
- 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 allowsApache License 2.0, 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
Apache License 2.0
Fully permissive: commercial use, redistribution, and derivatives allowed. Requires attribution and a copy of the license. Includes an express patent grant.
Identifiers
- Hugging Face
- poolside/Laguna-M.1
- Architecture
- Mixture of experts
- Takes in, gives back
- Text in, text out
- Catalogue slug
- poolside-laguna-m-1