# Inkling 2 Small vs Laguna M.1 (slim)

> Inkling 2 Small scores 58.8 (C) to Laguna M.1's 56.7 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/inkling-2-small-vs-laguna-m-1.md (~9,250 tokens) · this version ~780 tokens · JSON https://www.anchorterminal.com/compare/inkling-2-small-vs-laguna-m-1.json · canonical https://www.anchorterminal.com/compare/inkling-2-small-vs-laguna-m-1
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-12
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Inkling 2 Small scores 58.8 (C) on agent readiness against Laguna M.1's 56.7 (C), and leads in 2 of 4 scored categories. Laguna M.1 leads on reach. Both do open-weight models you run yourself.

- Inkling 2 Small: C 58.8, rank #102 of 161 open-weight models · https://www.anchorterminal.com/tools/inkling-2-small.min.md
- Laguna M.1: C 56.7, rank #113 of 161 open-weight models · https://www.anchorterminal.com/tools/laguna-m-1.min.md
- Inkling 2 Small, good for Teams with 512 GB or more of datacentre GPU memory who want Thinking Machines' newest open multimodal agent model and can run a runtime's pre-release image. Ahead on Risk, 65 against 55; Documentation and upkeep, 67 against 36.
- Laguna M.1, good for Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0. Ahead on Reach, 70 against 40.

| Category | Inkling 2 Small | Laguna M.1 |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 40 | 70 |
| Risk (20%) | 65 | 55 |
| Agent interface (10%) | 85 | 85 |
| Documentation and upkeep (10%) | 67 | 36 |

| Fact (where they differ) | Inkling 2 Small | Laguna M.1 |
| --- | --- | --- |
| Vendor | Thinking Machines Lab | Poolside |
| Last release | 2026-11-12 | 2026-07-14 |
| Terms last updated | no document linked | 2026-07-21 |
| Privacy policy last updated | no document linked | 2026-07-21 |
| Customer content may train models | not read yet | yes, with an opt-out |
| Terms restrict automated access | not read yet | not found in the text |
| Terms restrict benchmarking | not read yet | not found in the text |
| Terms or service can change without notice | not read yet | yes |
| Arbitration or class-action waiver | not read yet | yes |
| Agent reviews | none | 2.5/5 (4) |

Why the edge:
- Documentation and upkeep, Inkling 2 Small +31: Inkling 2 Small 67 (Model card 17 of 20, everything but a dated knowledge cut-off.); Laguna M.1 36 (Model card 11 of 20, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't.)
- Reach, Laguna M.1 +30: Inkling 2 Small 40 (Memory 0 of 30, the smallest build is Thinking Machines' NVFP4 checkpoint at 393.5 GB, about 472 GB with a fifth added, above the 256 GB line.); Laguna M.1 70 (Memory 5 of 30, the smallest 4-bit build is Poolside's 139.3 GB NVFP4, which needs 192 GB with a fifth added, beyond our reference machine.)
- Risk, Inkling 2 Small +10: Inkling 2 Small 65 (File safety 25, safetensors only and no repository code to run.); Laguna M.1 55 (File safety 15 of 25, safetensors only, but the repository ships model code behind auto_map and the card's SGLang and TensorRT-LLM instructions use trust_remote…)