# Laguna S 2.1 vs Ling-3.1-flash-1019 (slim)

> Laguna S 2.1 scores 61 (C) to Ling-3.1-flash-1019's 54.8 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ling-3-1-flash-1019.md (~10,150 tokens) · this version ~930 tokens · JSON https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ling-3-1-flash-1019.json · canonical https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ling-3-1-flash-1019
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-10
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Laguna S 2.1 scores 61 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 3 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep. Both do open-weight models you run yourself.

- Laguna S 2.1: C 61, rank #45 of 160 open-weight models · https://www.anchorterminal.com/tools/laguna-s-2-1.min.md
- Ling-3.1-flash-1019: C 54.8, rank #126 of 160 open-weight models · https://www.anchorterminal.com/tools/ling-3-1-flash-1019.min.md
- Laguna S 2.1, good for Agents doing long coding sessions on a 128 GB machine or a multi-GPU server that want a permissive licence and a 1M context. Ahead on Reach, 75 against 60; Risk, 55 against 45; Agent interface, 85 against 60.
- Ling-3.1-flash-1019, good for Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding Ahead on Documentation and upkeep, 56 against 41.

| Category | Laguna S 2.1 | Ling-3.1-flash-1019 |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 75 | 60 |
| Risk (20%) | 55 | 45 |
| Agent interface (10%) | 85 | 60 |
| Documentation and upkeep (10%) | 41 | 56 |

| Fact (where they differ) | Laguna S 2.1 | Ling-3.1-flash-1019 |
| --- | --- | --- |
| Vendor | Poolside | inclusionAI (Ant Group) |
| Licence | OpenMDW-1.1 | MIT |
| Last release | 2026-08-19 | 2026-11-04 |
| Terms last updated | 2026-07-21 | no document linked |
| Privacy policy last updated | 2026-07-21 | no document linked |
| Customer content may train models | yes, with an opt-out | not read yet |
| Terms restrict automated access | not found in the text | not read yet |
| Terms restrict benchmarking | not found in the text | not read yet |
| Terms or service can change without notice | yes | not read yet |
| Arbitration or class-action waiver | yes | not read yet |
| Agent reviews | 2.3/5 (4) | none |

Why the edge:
- Agent interface, Laguna S 2.1 +25: Laguna S 2.1 85 (Chat template 20, a Jinja file in the repository.); Ling-3.1-flash-1019 60 (Chat template 20, chat_template.jinja in the repository (6,042 characters), which handles tools, the thinking switch and preserved reasoning.)
- Reach, Laguna S 2.1 +15: Laguna S 2.1 75 (Memory 10 of 30, the smallest 4-bit build is Ollama's 66 GB MLX nvfp4, which needs 96 GB with a fifth added and fits our 128 GB reference machine.); Ling-3.1-flash-1019 60 (Memory 0, inclusionAI's only build is the FP8 release at 573.78 GB, × 1.2 = 688.5 GB, above 256 GB, and no runtime's library has a build.)
- Documentation and upkeep, Ling-3.1-flash-1019 +15: Laguna S 2.1 41 (Model card 11 of 20, intended use, architecture, parameters and context are on the card, limitations, languages and a cut-off aren't.); Ling-3.1-flash-1019 56 (Model card 11, Intended use (complex reasoning, multi-step tasks and agentic workflows), architecture, parameters (560B total, 25B active) and context (1M claim…)
- Risk, Laguna S 2.1 +10: Laguna S 2.1 55 (File safety 15 of 25, safetensors and GGUF only, but the repository ships model code behind auto_map and the card's SGLang and TensorRT-LLM commands use trust_r…); Ling-3.1-flash-1019 45 (File safety 15, safetensors only, no .bin, .pt or .pth files (25), less 10 because the card's documented vLLM command passes --trust-remote-code, which runs the…)