Poolside Open-Sourced a 225B Coding Model, and It's the Strongest US Open Weight Yet
Quick verdict
Poolside, a US coding-agent startup, just put its flagship model on Hugging Face under an Apache-2.0 license. Laguna-M.1 is a 225B mixture-of-experts model that scores 74.6% on SWE-bench Verified and 49.2% on the harder SWE-bench Pro. That puts it in the same conversation as the best open coding models, and people testing it on OpenRouter called it genuinely good and balanced. The notable part is who shipped it. Almost every truly open frontier-class coding model this year came from a Chinese lab. This one came from an American company giving away weights most labs would keep behind an API.
What actually shipped
Laguna-M.1 is a sparse mixture-of-experts model built for coding and agent work. The model card on Hugging Face lists 225B total parameters with about 23B active per token, 70 layers, 67 of them sparse MoE layers, 256 experts with top-k of 16, global attention, RoPE plus YaRN, and a 262,144-token context window. Poolside released both base and post-trained variants, and shipped weights in BF16, FP8, and NVFP4, so providers can serve it at the precision their hardware likes.
On benchmarks, the LocalLLaMA breakdown reports 74.6% on SWE-bench Verified, 63.1% on SWE-bench Multilingual, 49.2% on SWE-bench Pro, and 45.8% on Terminal-Bench 2.0. That lands it next to open models like Devstral 2 and GLM-4.7, while trailing DeepSeek-V4 Flash and Qwen3.5 on several of the listed metrics. So it is not topping every chart. The honest read is a strong, well-rounded coding model rather than a new outright leader.
Poolside also released a smaller sibling, Laguna-XS.2 at 33B total with 3B active, aimed at people who want something closer to local-runnable. That one is still waiting on llama.cpp support, tracked in a GitHub issue, so for now the easy path is a hosted endpoint rather than your own box.
Why it matters
The story this year has been Chinese labs owning open weights while American labs kept their best models closed. GLM, Kimi, MiniMax, Qwen: the permissive licenses kept coming from one side of the Pacific. Laguna-M.1 breaks that pattern. A commenter on the LocalLLaMA thread called it potentially the strongest open-weight coding model from a US-based company, and the room mostly agreed that a US lab shipping a flagship as open weights is underrated, even if the benchmark numbers do not lead the pack.
Apache-2.0 is the part that does the work here. It is one of the most permissive licenses around, which means you can use Laguna-M.1 commercially, fine-tune it, and ship it inside a product without the usage clauses that some other open releases attach. For teams that want a coding model they fully control, an Apache-2.0 license from a US company removes a set of legal and geopolitical worries that the Chinese open releases raise for some buyers.
The catch: open is not local
A 225B model is not something most people run at home. Even with only 23B active per token, you still have to hold all 225B parameters in memory, and at full precision that is hundreds of gigabytes. The FP8 and NVFP4 builds help, but you are still looking at serious hardware or, realistically, a hosted provider. The smaller Laguna-XS.2 is the one aimed at local use, and it is the one still blocked on llama.cpp support.
So for almost everyone the practical answer is the same as it was with GLM-5.2 and the Kimi and MiniMax releases: rent it from an inference provider rather than self-host. The value of open weights here is not that you personally run the model on your laptop. It is that the weights are out, several providers can serve them, and no single vendor controls whether the model stays available. That portability is the product, and it is worth more after a year of closed models getting pulled and restricted.
Video: Poolside Laguna M.1 tested
A hands-on look at how Laguna-M.1 holds up as a coding agent and where the benchmarks get murky.
FAQ
Can I run Laguna-M.1 on my own machine?
Probably not the 225B version. It needs hundreds of gigabytes of memory even in quantized form, so most people will use it through a hosted provider. The smaller Laguna-XS.2 at 33B is the local-friendly option, but it is still waiting on llama.cpp support. Our roundup of open-source AI models covers what is actually runnable at home.
Is Laguna-M.1 better than GLM-5.2 or Claude for coding?
Not clearly better. It scores well on SWE-bench but trails some open models on certain tests, and it has no published head-to-head against the closed frontier. The case for it is an Apache-2.0 license from a US lab plus solid all-round coding scores. For the closed leaders, see our best AI models for coding guide.
What does the Apache-2.0 license actually let me do?
You can use it commercially, modify it, fine-tune it, and ship it inside your own product without the extra usage restrictions some open releases carry. That permissiveness is a big part of why this release got attention beyond the benchmark numbers.
Sources
- Hugging Face - poolside/Laguna-M.1 model card and specs
- Hugging Face - poolside/Laguna-XS.2, the smaller 33B variant
- r/LocalLLaMA - Laguna-M.1 specs, benchmarks, and the open-weight discussion
- GitHub - llama.cpp support tracking for Laguna-XS.2
Further reading
Try all the models mentioned in this article
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