# EXAONE 4.5 33B vs LBS-27B (slim)

> EXAONE 4.5 33B scores 61 (C) to LBS-27B's 55.8 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lbs-27b.md (~9,000 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lbs-27b.json · canonical https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lbs-27b
- 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
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

EXAONE 4.5 33B scores 61 (C) on agent readiness against LBS-27B's 55.8 (C), and leads in 3 of 4 scored categories. 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
- LBS-27B: C 55.8, rank #118 of 160 open-weight models · https://www.anchorterminal.com/tools/lbs-27b.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 Reach, 65 against 60; Agent interface, 85 against 70; Documentation and upkeep, 50 against 43.
- LBS-27B, good for Agents with about 96 GB of GPU memory that need to find click targets on dense professional screenshots, or read fine detail in large images, and can run a small crop loop and wait for independent results.

| Category | EXAONE 4.5 33B | LBS-27B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 65 | 60 |
| Risk (20%) | 50 | 50 |
| Agent interface (10%) | 85 | 70 |
| Documentation and upkeep (10%) | 50 | 43 |

| Fact (where they differ) | EXAONE 4.5 33B | LBS-27B |
| --- | --- | --- |
| Vendor | LG AI Research | inclusionAI (Ant Group) |
| Licence | EXAONE AI Model License Agreement 1.2 - NC (research and education only) | Apache-2.0 (the card's metadata and licence section; the LICENSE file sits in the GitHub repository inclusionAI/UI-Venus on its LBS branch, not in the Hugging Face repository) |
| Last release | 2026-07-06 | 2026-10-26 |
| Terms last updated | not read yet | no document linked |

Why the edge:
- Agent interface, EXAONE 4.5 33B +15: EXAONE 4.5 33B 85 (Chat template 20, chat_template.jinja in the repository (5,930 characters).); LBS-27B 70 (The chat template earns 20, Qwen's Jinja file in the repository.)
- Documentation and upkeep, EXAONE 4.5 33B +7: 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…); LBS-27B 43 (The model card earns 8, with intended use, one limitation and parameters, and no architecture, context, languages or cut-off.)
- Reach, EXAONE 4.5 33B +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.); LBS-27B 60 (Memory earns 10, since the only build is 54.73 GB of BF16, about 66 GB with a fifth added, which fits the reference machine but not 64 GB.)