Laguna S 2.1
The West's most capable open-weight model for agentic coding
Context window
1M
Input / 1M tokens
Not announced
Output / 1M tokens
Not announced
Provider
Poolside
Data verified 2026-07-22
A 118-billion-parameter open-weight Mixture-of-Experts (MoE) foundation model built for agentic coding. It activates 8 billion parameters per token and supports a 1 million token context window. The model matches or exceeds models several times its size on long-horizon coding benchmarks.
Capability index
Relative estimates (0-100) to place this model against its peers, grounded in published benchmarks.
How to access it
Open-weight model available on Hugging Face under OpenMDW-1.1 license. Can be run locally on NVIDIA DGX Spark or accessed via OpenRouter.
Strengths
- ✓Exceptional performance on agentic coding benchmarks relative to model size
- ✓Efficient inference with only 8B active parameters despite 118B total
- ✓Supports 1M token context window
- ✓Small enough to run locally on single NVIDIA DGX Spark
- ✓Fully open-weight under permissive OpenMDW-1.1 license
Best for developers who...
When to choose it (and when not to)
Reach for Laguna S 2.1 when...
- →Need open-weight coding model with strong benchmarks
- →Want to self-host rather than use API
- →Require 1M context window for coding tasks
- →Seeking competitive alternative to closed-source models
Look elsewhere if...
- ✕Require state-of-the-art frontier performance
- ✕Need general-purpose reasoning beyond coding
- ✕Can only use closed-source models
How to use it
- ›Leverage thinking mode for complex multi-step coding problems
- ›Utilize full 1M context window for long-context code understanding
- ›Include agentic harness (pool) for autonomous coding task execution
Quickstart
Pythonfrom transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained('poolside/laguna-s-2.1')
model = AutoModelForCausalLM.from_pretrained('poolside/laguna-s-2.1')Weights also available in BF16, FP8, INT4, NVFP4 formats on Hugging Face
API model id: laguna-s-2-1
Benchmarks
| Benchmark | Score | Notes |
|---|---|---|
| Terminal-Bench 2.1 | 70.2% | With thinking mode enabled; 60.4% without thinking |
| SWE-Bench Multilingual | 78.5% | Leads open disclosed-size models |
| SWE-Bench Pro | ~60% | Matches or exceeds larger models |
| DeepSWE v1.1 | 40.4% | With roughly 1/6 the active parameters of DeepSeek-V4-Pro-Max (9.0%) |
Source: Poolside Official Announcement
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