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Research · Jul 24, 2026, 02:05 AM · 4 sources

Poolside releases Laguna S 2.1, 118B-parameter open-weight model matching closed competitors

Poolside released Laguna S 2.1, a 118-billion-parameter open-weight Mixture-of-Experts model for agentic coding, on July 21, 2026. The model activates only 8 billion parameters per token, supports a 1-million-token context window, and is available on Hugging Face under the OpenMDW-1.1 license. Weights are available in multiple formats (BF16, FP8, INT4, NVFP4, GGUF, MLX) and the model runs on a single NVIDIA DGX Spark. Poolside trained the model in under nine weeks on 4,096 H200 GPUs.

On Terminal-Bench 2.1 (agentic terminal tasks), Laguna S 2.1 scored 70.2% with thinking mode enabled—beating DeepSeek V4 Pro Max (1.6T total, 49B active) at 64.0% and NVIDIA Nemotron 3 Ultra (550B, 55B active) at 56.4%. On SWE-Bench Pro, it achieved 59.4% versus DeepSeek V4 Pro Max at 55.4%. Most dramatically, on DeepSWE (harder multi-file tasks), Laguna S 2.1 scored 40.4% while DeepSeek V4 Pro Max scored just 9.0%—a 31.4-point gap that suggests Poolside's emphasis on verification and persistence behaviors outperforms raw model size.

Laguna S 2.1 is the first Western open-weight model in its size class released in 11 months—the last comparable release was OpenAI's gpt-oss-120b in August 2025. Poolside co-CEO Jason Warner framed it as a response to Chinese dominance in open-weight systems (DeepSeek, Qwen, Kimi) and marketed it as "the West needs open-weight models it can trust." Pricing is $0.10/$0.20 per million input/output tokens on OpenRouter, or free locally if you have hardware.

For developers: Laguna S 2.1 demonstrates that parameter-efficient training (8B active, trained in <9 weeks) can outcompete larger dense and MoE models on real agentic benchmarks, validating the efficiency thesis. However, Poolside acknowledges the model is "not yet at the frontier"—closed-source leaders (OpenAI, Anthropic) still lead by ~10–15 points on Terminal-Bench. The test now is whether Terminal-Bench scores translate to production performance on messy, non-curated codebases, and whether Poolside can maintain the pace against Chinese labs that are also improving rapidly.

Sources

Everything this brief rests on
  1. 01 Primary source latent.space
  2. 02 latent.space latent.space “Laguna S 2.1: 118B total, 8B active, 70.2% Terminal-Bench with thinking, beats DeepSeek V4 Pro Max; $0.10/$0.20 per M tokens on OpenRouter”
  3. 03 thenextweb.com thenextweb.com “Trained under 9 weeks on 4,096 H200s; first Western 118B-class model in 11 months; open weights under OpenMDW-1.1”
  4. 04 benchlm.ai benchlm.ai “Terminal-Bench: Laguna S 2.1 70.2% vs DeepSeek V4 Pro Max 64.0%; SWE-Bench Pro: 59.4% vs 55.4%; DeepSWE: 40.4% vs 9.0%”