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Model Details
Model Provider
bullerwins
Model Tree
poolside/Laguna-S-2.1
Input Modalities
Output Modalities
Supported Functionality
NeelNanda/pile-10k; evaluation prompts
and reference logits were never used for calibration or layer selectionKLD is measured as mean KL(BF16 reference || quantized model) over fixed
1,024-token windows from held-out WikiText-2, GSM8K, and MBPP text; lower is
better. We explored group sizes from G128 through G8, RTN and SignRound,
expert-only versus all-linear quantization, and architecture-motivated BF16
exceptions. Seven load-verified, mathematically non-dominated checkpoints were
retained. G8 achieved the lowest deterministic development KLD (0.241321) at
93.411 GiB, while this G16 checkpoint was selected as the practical default
because it saves 16.52 GiB and has comparable monitored holdout fidelity.
G16/G32/G64 measurements use vLLM's Humming WNA16 backend. Fresh-engine noise was approximately 0.004 KLD, so small differences inside the reported ranges should not be treated as meaningful.
The chart shows the retained size/KLD frontier and the Poolside NVFP4 and FP8 reference checkpoints. Vertical bars represent observed fresh-engine ranges.
The original Poolside model card is reproduced below.
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.
enable_thinking| 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 |
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.
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.
shell
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}'
[!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"}'.
shell
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
shell
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).
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.)
shell
git clone --branch laguna https://github.com/poolsideai/llama.cppcd 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 7 -fa on --jinja --port 8000
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:
python
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.
This model is licensed under the OpenMDW-1.1 License.
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.
| 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% | - | - |