poolside/Laguna-XS-2.1

One-line: 33B MoE (3B active) agentic coding model with SWE-bench Verified 70.9% — purpose-built for local agent loops.

Coding benchmarks

BenchmarkScoreSource
HumanEval——
HumanEval+——
MBPP——
LiveCodeBench——
Aider polyglot——
SWE-bench Verified70.9%poolside (lab-reported, Harbor Framework)
SWE-bench Multilingual63.1%poolside (lab-reported, Harbor Framework)
SWE-bench Pro47.6%poolside (lab-reported)
Terminal-Bench 2.037.5%poolside (lab-reported)

Agentic / tool-use benchmarks

BenchmarkScoreSource
BFCL——
τ-Bench——
ToolACE——
GAIA——
WebArena / Mind2Web——

Reasoning (context only)

BenchmarkScoreSource
MMLU / MMLU-Pro——
GPQA Diamond——
MATH——
AIME 2025——

Efficiency

  • Active params: 3B (total 33B MoE: 256 experts + 1 shared)
  • Context length: 262,144 tokens
  • VRAM (fp16 rough): ~6 GB (active weights only)

What makes it notable

Laguna XS 2.1 is poolside’s second-gen 3B-active MoE explicitly designed for agentic coding on local hardware — 33B total params, 262K context, FP8 KV cache, mixed SWA/global attention (3:1 across 40 layers), native reasoning with interleaved thinking between tool calls. Beats its predecessor Laguna XS.2 by +5.4pp on SWE-bench Multilingual (63.1% vs 57.7%) and leads all open-weight models <70B on SWE-bench Verified (70.9%) per the lab’s Harbor Framework runs (mean pass@1 over 4 attempts). Launch-day support in vLLM, SGLang, Transformers, TRT-LLM, llama.cpp, Ollama. OpenMDW-1.1 license (commercial-friendly). The DFlash 5-layer speculator (~70% acceptance on coding) is available for speculative decoding. This is the strongest open-weight agentic coding model in the 1B–70B active-param range for local deployment.

See also