# EXAONE 4.5 33B vs Laguna S 2.1 (slim)

> EXAONE 4.5 33B and Laguna S 2.1 score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

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- 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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EXAONE 4.5 33B and Laguna S 2.1 score within a point of each other on agent readiness, 61 (C) and 61 (C). Laguna S 2.1 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 S 2.1: C 61, rank #45 of 160 open-weight models · https://www.anchorterminal.com/tools/laguna-s-2-1.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 41.
- 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 65; Risk, 55 against 50.

| Category | EXAONE 4.5 33B | Laguna S 2.1 |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 65 | 75 |
| Risk (20%) | 50 | 55 |
| Agent interface (10%) | 85 | 85 |
| Documentation and upkeep (10%) | 50 | 41 |

| Fact (where they differ) | EXAONE 4.5 33B | Laguna S 2.1 |
| --- | --- | --- |
| Vendor | LG AI Research | Poolside |
| Licence | EXAONE AI Model License Agreement 1.2 - NC (research and education only) | OpenMDW-1.1 |
| Last release | 2026-07-06 | 2026-08-19 |
| 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 | 2.3/5 (4) |

Why the edge:
- Reach, Laguna S 2.1 +10: 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 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.)
- Documentation and upkeep, EXAONE 4.5 33B +9: 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 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.)
- Risk, Laguna S 2.1 +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 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…)