Poolside just dropped a model that punches three weight classes above its actual runtime cost — and they’re putting the benchmark trajectories online to prove it.
What Is Laguna S 2.1?
Released today, Laguna S 2.1 is a 118B total parameter Mixture-of-Experts (MoE) model with 8B activated parameters per token. The MoE architecture is key: you get 118B-scale capacity, but because only 8B parameters are active for any given forward pass, the runtime cost and inference speed look much more like an 8B model than a 100B+ dense model.
It supports a context window of up to 1 million tokens in both thinking and no-thinking modes — competitive with the largest context windows available anywhere in the market.
The full model went from start-of-training to launch in under nine weeks. For context, that’s an aggressive timeline for a 118B parameter model.
License: OpenMDW-1.1 — permissive for commercial use. (Note: earlier Poolside models like Laguna XS.2 used Apache 2.0; Laguna S 2.1 uses OpenMDW-1.1.)
Benchmark Performance
Poolside has consistently trained on long-horizon agentic coding tasks, and the benchmarks reflect that focus:
| Benchmark | Score |
|---|---|
| Terminal-Bench 2.1 | 70.2% |
| SWE-Bench Multilingual | 78.5% |
| SWE-Bench Pro | 59.4% |
| DeepSWE | 40.4% |
These numbers sit competitively against models many times its size. SWE-Bench Multilingual at 78.5% — covering implementation across multiple programming languages — is particularly notable for a model with only 8B active parameters.
Poolside is doing something interesting with benchmark transparency: for every published score, they’re releasing full trajectories for every trial at trajectories.poolside.ai. That means you can inspect not just the final scores but the actual model behavior on each task. That level of openness is unusual and raises the bar for how benchmark claims should be supported.
Runs on a Single DGX Spark
The hardware story is part of the product story. Laguna S 2.1 can run on a single NVIDIA DGX Spark with quantization. The DGX Spark is NVIDIA’s compact standalone AI workstation — not a multi-node cluster, not a cloud-only setup. A 118B MoE model that fits on a single DGX Spark is genuinely accessible to teams that want on-premises or air-gapped deployments.
With quantization applied, the inference profile becomes practical for real production agentic coding workflows — not just research benchmarks.
Where to Get It
Laguna S 2.1 is available through:
- Hugging Face: poolside/Laguna-S-2.1
- OpenRouter: Listed alongside major frontier models
- Vercel AI Gateway: Available for production deployments
The Broader Poolside Story
Poolside has been building specifically for agentic coding — models that can handle long-horizon tasks, maintain context across large codebases, and execute multi-step software engineering work. Laguna S 2.1 is their third model in three months, a cadence that suggests a deliberate iteration strategy rather than occasional major releases.
The 1M context window is central to that bet. Long-horizon agentic coding requires holding substantial context: existing code, test results, prior decisions, and multi-step plans. At 1M tokens, you can work with significantly larger codebases and longer task horizons than most models allow.
For open-weight model users who need strong agentic coding performance without sending code to external APIs, Laguna S 2.1 is a compelling option worth evaluating — especially given the trajectory transparency that lets you verify the benchmark claims yourself.
Sources
- Introducing Laguna S 2.1 — Poolside Blog (July 21, 2026)
- Laguna S 2.1 on Hugging Face
- Benchmark Trajectories
Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260721-2000
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