Hugging Face に「Laguna S 2.1」公開

Use on OpenRouter ·

Use on Vercel AI Gateway ·

Release blog post

Laguna S 2.1 is a 118B total parameter Mixture-of-Experts model with 8B activated

parameters per token, designed for agentic coding and long-horizon work. It sits

between Laguna XS 2.1 (33B-A3B) and

Laguna M.1 (225B-A23B) in the Laguna series and shares the family recipe: a

token-choice router with softplus gating over 256 routed experts plus one shared

expert, grouped-query attention, and interleaved full/sliding-window attention.

Highlights

• Mixed SWA and global attention layout: 48 layers in a 1:3 global-to-SWA ratio

(12 global attention layers, 36 sliding-window layers, window 512), with softplus

attention gating and per-layer-type rotary scales

• 1M context: 1,048,576-token context window

• Native reasoning support: interleaved thinking between tool calls, with

per-request control via enable_thinking

• Speculative decoding: a trained

DFlash draft model is available

for lower-latency serving

• Quantized variants:

FP8,

NVFP4,

INT4 and

GGUF

• OpenMDW-1.1 license: Use and modify the model and associated materials freely

for commercial and non-commercial purposes

(learn more about OpenMDW)

Model overview

• Number of parameters: 118B total, ~8B activated per token

• Layers: 48 (12 global attention, 36 sliding-window attention)

• Experts: 256 routed (top-10) plus 1 shared expert

• Attention: grouped-query, 8 KV heads, head dim 128; per-head softplus output gating

• Sliding window: 512 tokens

• Context window: 1,048,576 tokens

• Vocabulary: 100,352 tokens (Laguna family tokenizer)

• Modality: text-to-text

• Reasoning: interleaved thinking with preserved thinking

Benchmark results

Model

Size

Terminal-Bench 2.1

SWE-bench Multilingual

SWE-Bench Pro (Public Dataset)

DeepSWE

SWE Atlas (Codebase QnA)

Toolathlon Verified

Laguna S 2.1

118B-A8B

70.2%

78.5%

59.4%

40.4%

46.2%

49.7%

Tencent Hy3

295B-A21B

71.7%

75.8%

57.9%

-

-

-

Inkling

975B-A41B

63.8%

-

54.3%

-

-

45.5%*

Nemotron 3 Ultra

550B-A55B

56.4%

67.7%

-

-

-

34.3%*

DeepSeek-V4-Pro Max

1.6T-A49B

64.0%*

76.2%

55.4%

9.0%*

27.2%*

55.9%*

Kimi K3

2800B-A50B

88.3%

-

-

69%

-

-

Qwen 3.7 Max

-

74.5%*

78.3%

60.6%

-

-

-

Muse Spark 1.1

-

80%

-

61.5%

53.3%

42.2%*

75.6%

Claude Fable 5

-

88%

-

80.3%

70%

-

-

Benchmarks as of 21 July 2026. Laguna S 2.1 in bold; a dash (-) marks a benchmark a model was not evaluated on. Scores marked * are as reported by third parties: Terminal-Bench 2.1 and DeepSWE via Artificial Analysis, SWE Atlas via Scale AI's official leaderboard, and Toolathlon Verified via its official leaderboard. Full evaluation trajectories: trajectories.poolside.ai.

Usage

Laguna S 2.1 uses the same laguna architecture as Laguna XS 2.1, so the same

engine integrations apply (vLLM, SGLang, Transformers, TRT-LLM, llama.cpp). At 118B

parameters the BF16 checkpoint needs multiple GPUs (roughly 236GB of weights);

quantized variants reduce this substantially.

vLLM

vllm serve \
    --model poolside/Laguna-S-2.1 \
    --tensor-parallel-size 4 \
    --tool-call-parser poolside_v1 \
    --reasoning-parser poolside_v1 \
    --enable-auto-tool-choice \
    --served-model-name laguna \
    --default-chat-template-kwargs '{"enable_thinking": true}'

Optional: speculative decoding with DFlash. Pair with the

Laguna S 2.1 DFlash draft model

by adding

--speculative-config '{"model":"poolside/Laguna-S-2.1-DFlash","num_speculative_tokens":7,"method":"dflash"}'.

SGLang

python -m sglang.launch_server \
  --model-path poolside/Laguna-S-2.1 \
  --tp-size 4 \
  --reasoning-parser poolside_v1 \
  --tool-call-parser poolside_v1 \
  --trust-remote-code

TRT-LLM

trtllm-serve poolside/Laguna-S-2.1 --trust-remote-code \
    --tool_parser poolside_v1 --reasoning_parser laguna

Note the flag names differ from vLLM's (--tool_parser, and the reasoning parser

is laguna, not poolside_v1).

llama.cpp

GGUF conversions are available at

poolside/Laguna-S-2.1-GGUF.

Serve with poolside's llama.cpp fork, branch

laguna, which carries

full Laguna support including DFlash speculative decoding. (Base Laguna support

is also in upstream review:

ggml-org/llama.cpp#25165.)

git clone --branch laguna https://github.com/poolsideai/llama.cpp
cd llama.cpp && cmake -B build && cmake --build build -j

./build/bin/llama-server -m laguna-s-2.1-Q4_K_M.gguf --jinja --port 8000

# with DFlash speculative decoding:
./build/bin/llama-server -m laguna-s-2.1-Q4_K_M.gguf \
  -md laguna-s-2.1-DFlash-BF16.gguf \
  --spec-type draft-dflash --spec-draft-n-max 15 -fa on --jinja --port 8000

Controlling reasoning

Laguna S 2.1 has native reasoning support and works best with *preserved thinking*:

keep reasoning_content from prior assistant messages in the message history.

The model will generally reason before calling tools and between tool calls, and

may stop reasoning in follow-up steps if prior thinking blocks are dropped.

Thinking is controlled per request via the chat template:

extra_body={"chat_template_kwargs": {"enable_thinking": False}}

or at the server level with

--default-chat-template-kwargs '{"enable_thinking": true}'. For agentic coding

use cases we recommend enabling thinking and preserving reasoning in the message

history.

License

This model is licensed under the OpenMDW-1.1 License.

Intended and Responsible Use

Laguna S 2.1 is designed for software engineering and agentic coding use cases, and you are responsible for confirming that it is appropriate for your intended application. Laguna S 2.1 is subject to the OpenMDW-1.1 License, and should be used consistently with Poolside's Acceptable Use Policy. We advise against circumventing Laguna S 2.1 safety guardrails without implementing substantially equivalent mitigations appropriate for your use case.

Please report security vulnerabilities or safety concerns to security@poolside.ai.