American AI startup Poolside launches free, high-performing open model Laguna XS.2 for local agentic coding

The AI race lately has felt a bit like a game of tennis: first, Anthropic releases a new, pricey state-of-the-art proprietary model for general users (Claude Opus 4.7), then, a week or so later, its rival OpenAI volleys back with one of its own (GPT-5.5). And all the while, Chinese companies like DeepSeek and even Xiaomi are seeking to appeal to users by playing a different game: nearing the frontier, but with open licensing and far lower costs.

So it's a big surprise when a new, affordable, highly performant open source contender from the U.S. emerges. Today, we got one from the smaller, lesser-known U.S. AI startup, Poolside, founded in San Francisco in 2023.

The company launched its two new Laguna large language models, both of which offer affordable intelligence optimized for agentic workflows (AI that does more than just chat or generate content, but can, in this case, write code, use third-party tools, and take actions autonomously), as well as a new coding agent harness called (fittingly) "pool" and a new web-based, mobile optimized agentic coding development and interactive preview environment, "shimmer," which lets you write code with the Laguna models on the go.

The new AI models that Poolside released today include:

Laguna M.1: a proprietary 225-billion parameter Mixture of Experts (MoE) model with 23 billion active parameters. This flagship model is optimized for high-consequence enterprise and government environments, designed to solve complex, long-horizon software engineering problems that require maximum reasoning and planning capabilities.Laguna XS.2: an Apache 2.0 open licensed 33-billion parameter MoE with 3 billion active. Engineered for efficiency and community innovation, this model is designed for local agentic coding tasks and provides a versatile foundation for developers looking to fine-tune, quantize, or serve powerful agents on a single GPU. In other words, developers can download and run Laguna XS.2 on their desktop or even laptop computers without an internet connection — completely private and secured.

Notably, as mentioned above, only the smaller of the two models, XS.2, is available now under an open source Apache 2.0 license (on Hugging Face) — yet Poolside is offering even the larger M.1 for free temporarily through its API and third-party distribution partners, OpenRouter, Ollama, and Baseten, making it a great use case for developers who wish to test it out.

Also noteworthy: the two new Lagunas were trained from scratch — not fine-tuned/post-trained base models from Chinese giant Alibaba's Qwen series like some other U.S. labs have pursued lately.

As Poolside wrote in a blog post today, it's spent the last few years "focused on serving our government and public sector clients with capable models deployable into the highest-security environments," yet is now going open source "to support builders and the wider research community."

When I asked on X why government agencies would seek to use Poolside instead of leading proprietary U.S. labs like Anthropic, OpenAI and Google, Poolside post-training engineer George Grigorev told me in a reply that: "we think that we can be faster to deploy our models to enterprise customers, and we can literally ship weights in fully isolated environments on-prem, so it can work offline. which might be critical for gov/public sectors :) but ofc anthropic enterprise is hard to beat"

How Poolside's Laguna M.1 and Laguna XS.2 were trained

Poolside constructs its AI models within a specialized digital environment called the "Model Factory".

At the heart of this process is Titan, the company's powerful internal software that serves as the "furnace" for training. To help the AI learn as efficiently as possible, Poolside uses a unique tool called the Muon optimizer.

Think of Muon as a high-speed tutor; it helps the model master new information approximately 15% faster than standard industry methods, a critical gain when training at the 30-trillion-token scale.

It achieves this by ensuring that every update to the model's "brain" is mathematically balanced and pointing in the right direction, which prevents the AI from getting confused or stuck during its intensive training sessions.

The information used to train these models—a staggering 30 trillion "tokens" or pieces of data—is carefully selected using a system called AutoMixer.

Rather than just feeding the AI everything it finds on the internet, AutoMixer leverages a a "swarm" of sixty proxy models on different data mixes to scientifically determine which combination of code, math, and general web data produces the best reasoning capabilities.

In this way, it acts like a master chef, scientifically testing thousands of different "recipes" to find the perfect balance of computer code, mathematics, and general knowledge.

While much of this data comes from the public web, about 13% of it is "synthetic data". This is high-quality, custom-made practice material created by other AIs to teach the models specific skills that are difficult to find in the real world.

Once the model has finished its basic "schooling," it enters a virtual gym for Reinforcement Learning. In this stage, the AI practices solving real software engineering problems in a safe, isolated digital playground. It learns through trial and error, receiving a "reward" or positive signal every time it successfully fixes a bug or writes a working piece of code. This constant cycle of practice and feedback is what transforms the AI from a simple text generator into a capable "agent" that can plan and execute complex, multi-step projects just like a human software engineer.

While M.1 represents the peak of Poolside’s current research, the smaller Laguna XS.2 may be the more disruptive entry.

At just 33 billion total parameters (3 billion activated), XS.2 is a "second-generation" MoE model that incorporates everything the team learned from training M.1.

Benchmarks show Poolside's Laguna models punch far above their weight class

Langua M.1's performance on the SWE-bench Pro—a benchmark designed to test an AI’s ability to solve real-world software issues—reached **46.9% on SWE-bench Pro, **nearing the performance of the far-larger Qwen-3.5 and DeepSeek V4-Flash.

Despite being a fraction of the size, Laguna XS.2 achieves a 44.5% score on SWE-bench Pro, nearly matching its larger sibling.

On the SWE-bench Verified track, M.1 scored 72.5%, outperforming the dense Devstral 2 (72.2%) but trailing Claude Sonnet 4.6, which leads the category at 79.6%.

These results highlight M.1’s specialization in long-horizon software tasks, particularly those involving complex planning across interconnected files.

The smaller Laguna XS.2 exhibits remarkable efficiency, nearly matching the performance of its much larger sibling on high-consequence tasks. Despite having only 3B active parameters, XS.2 surpasses Claude Haiku 4.5 (39.5%) and the significantly larger Gemma 4 31B dense model (35.7%) on** SWE-bench Pro. **

In terminal-based reasoning, XS.2’s 30.1% on Terminal-Bench 2.0 also edges out Haiku 4.5’s 29.8%, although it remains behind specialized "nano" models such as GPT-5.4 Nano, which reached 46.3% on the same benchmark.

Collectively, these benchmarks suggest that Poolside’s focus on agentic RL and synthetic data curation has allowed its smaller models to "punch up" into weight classes typically reserved for far denser architectures.

While top-tier proprietary models like Claude Sonnet 4.6 maintain a lead in overall success rates, the Laguna family—particularly the open-weight XS.2—offers a competitive alternative for developers who prioritize local execution and customizable agent workflows.

All benchmarking was conducted using the Harbor Framework with sandboxed execution, ensuring that the results reflect the models' ability to function in realistic, resource-constrained environments.