# Laguna S 2.1

URL: https://interfaze.ai/models/poolsidelaguna-s-21

Laguna S 2.1 by poolside, a text-generation model. Understand and compare features, benchmarks, and capabilities.

## Comparison

| Feature | Laguna S 2.1 | Interfaze |
| --- | --- | --- |
| Input Modalities | text | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | Yes | Yes |
| Language Support | unknown | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | Yes | Yes |
| Context Input Size | 1M | 1M |
| Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search |

### Scaling

| Feature | Laguna S 2.1 | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)

View model card on [Hugging Face](https://huggingface.co/poolside/Laguna-S-2.1)

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](https://huggingface.co/poolside/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](https://huggingface.co/poolside/Laguna-S-2.1-DFlash) is available for lower-latency serving
-   **Quantized variants**: [FP8](https://huggingface.co/poolside/Laguna-S-2.1-FP8), [NVFP4](https://huggingface.co/poolside/Laguna-S-2.1-NVFP4), [INT4](https://huggingface.co/poolside/Laguna-S-2.1-INT4) and [GGUF](https://huggingface.co/poolside/Laguna-S-2.1-GGUF)
-   **OpenMDW-1.1 license**: Use and modify the model and associated materials freely for commercial and non-commercial purposes ([learn more about OpenMDW](https://openmdw.ai/))

## 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](https://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}'
```

> \[!NOTE\] **Optional: speculative decoding with DFlash.** Pair with the [Laguna S 2.1 DFlash draft model](https://huggingface.co/poolside/Laguna-S-2.1-DFlash) 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](https://huggingface.co/poolside/Laguna-S-2.1-GGUF). Serve with poolside's llama.cpp fork, branch [`laguna`](https://github.com/poolsideai/llama.cpp/tree/laguna), which carries full Laguna support including DFlash speculative decoding. (Base Laguna support is also in upstream review: [ggml-org/llama.cpp#25165](https://github.com/ggml-org/llama.cpp/pull/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


./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](https://huggingface.co/poolside/Laguna-S-2.1/blob/main/LICENSE.md).

## 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](https://huggingface.co/poolside/Laguna-S-2.1/blob/main/LICENSE.md), and should be used consistently with Poolside's [Acceptable Use Policy](https://poolside.ai/legal/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](mailto:security@poolside.ai).

## Want more deterministic results?

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)