Poolside Releases Laguna S 2.1, an Open-Weight Agentic Coding Model Punching Above Its Weight Class on SWE-Bench Multilingual

Laguna S 2.1, an Open-Weight Agentic Coding Model Punching Above Its Weight Class on SWE-Bench Multilingual">

Poolside has released Laguna S 2.1 , a 118B-parameter open-weight model built for agentic coding. It is a Mixture-of-Experts (MoE) model with 8B activated parameters per token. It supports a context window of up to 1M tokens in both thinking and no-thinking modes. The weights are on Hugging Face under an OpenMDW-1.1 license, and the model is small enough to run on a single NVIDIA DGX Spark .

On long-horizon coding benchmarks, Laguna S 2.1 holds its own against models several times its size, including DeepSeek-V4-Pro-Max, NVIDIA’s Nemotron 3 Ultra, and Thinking Machines’ Inkling. Laguna S 2.1 is a scale-up of the Laguna XS family, trained on the same pre-training data as XS 2.1.

The model activates roughly 6.8% of its parameters on any given token. All 118B parameters remain resident in memory, but only ~8B route through the network per step. That sparsity is why a mid-size model can behave like a larger one while staying cheap to serve.

Poolside team publishes weights in BF16, FP8, INT4, and NVFP4, along with official GGUF and MLX conversions and DFlash draft models. It went from the start of training to launch in under nine weeks. Pre-training began on 22 May 2026 on 4,096 NVIDIA H200 GPUs. It is the first Poolside model where reinforcement learning ran in FP8 precision.

Laguna S 2.1 scores 70.2% on Terminal-Bench 2.1 with thinking enabled. That places it first among open, disclosed-size models on Poolside’s compiled leaderboard, behind only larger or closed systems. On SWE-Bench Multilingual it scores 78.5%, topping the published table outright. The full comparison Poolside released is below.

The clearest signal is DeepSWE v1.1, which still has real headroom. There, Laguna S 2.1 scores 40.4% against DeepSeek-V4-Pro-Max’s 9.0%, with roughly one-sixth the active parameters. Closed frontier models such as Claude Fable 5 and Kimi K3 still lead on several benchmarks. Poolside’s claim is about the weight class, not the outright top. Trajectories from the final evaluation set is published at trajectories.poolside.ai .

Laguna S 2.1 has two modes: off and max , with max enabled by default. In max mode the model sets its own test-time compute budget. Poolside is shipping without user-configurable low/medium/high effort control for now.

Max thinking lifts Terminal-Bench 2.1 from 60.4% to 70.2%. It lifts DeepSWE from 16.5% to 40.4%. Those gains cost tokens: DeepSWE trajectories run about 249k completion tokens in thinking mode against 99k without. Poolside team reports coherent, productive reasoning running for hours and hundreds of thousands of tokens.