# EXAONE 4.5 33B vs Laguna M.1 (slim)

> EXAONE 4.5 33B scores 61 (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.

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EXAONE 4.5 33B scores 61 (C) on agent readiness against Laguna M.1's 56.7 (C), and leads in 1 of 4 scored categories. Laguna M.1 leads on reach and risk. Both do open-weight models you run yourself.

- EXAONE 4.5 33B: C 61, rank #61 of 160 open-weight models · https://www.anchorterminal.com/tools/exaone-4-5-33b.min.md
- Laguna M.1: C 56.7, rank #112 of 160 open-weight models · https://www.anchorterminal.com/tools/laguna-m-1.min.md
- EXAONE 4.5 33B, good for Researchers and students working on documents, Korean or multimodal agents who don't need commercial rights Ahead on Documentation and upkeep, 50 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 65; Risk, 55 against 50.

| Category | EXAONE 4.5 33B | Laguna M.1 |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 65 | 70 |
| Risk (20%) | 50 | 55 |
| Agent interface (10%) | 85 | 85 |
| Documentation and upkeep (10%) | 50 | 36 |

| Fact (where they differ) | EXAONE 4.5 33B | Laguna M.1 |
| --- | --- | --- |
| Vendor | LG AI Research | Poolside |
| Licence | EXAONE AI Model License Agreement 1.2 - NC (research and education only) | Apache-2.0 |
| Last release | 2026-07-06 | 2026-07-14 |
| Terms last updated | not read yet | 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, EXAONE 4.5 33B +14: EXAONE 4.5 33B 50 (Model card 20, Uses (the quickstart's question answering, document and agent examples), limitations, the architecture, the parameters, the context, the language…); 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 +5: EXAONE 4.5 33B 65 (Memory 20, the smallest build at 4 bits is LG's IQ4_XS GGUF, 18.03 GB with the 2.57 GB projector, and 20.60 GB × 1.2 = 24.7 GB, within 32 GB.); 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, Laguna M.1 +5: EXAONE 4.5 33B 50 (File safety 25, safetensors and GGUF only, no .bin, .pt or .pth files and no Python in the repository.); 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…)