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
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
benchmarks
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.
[!NOTE]
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"}'.
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.)
Quantization variants are available as tags (q4_K_M, q8_0, f16, mxfp8,
nvfp4, mlx-bf16), for example ollama run laguna-s-2.1:q8_0. The Laguna chat
template is baked into the model, so tool-calling and interleaved reasoning work
automatically.
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:
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.
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.
