# EXAONE 4.5 33B vs GLM-5.3-Flash (slim)

> GLM-5.3-Flash scores 65.1 (B) to EXAONE 4.5 33B's 61 (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-glm-5-3-flash.md (~9,650 tokens) · this version ~830 tokens · JSON https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-glm-5-3-flash.json · canonical https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-glm-5-3-flash
- 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)

GLM-5.3-Flash scores 65.1 (B) on agent readiness against EXAONE 4.5 33B's 61 (C), and leads in 2 of 4 scored categories. EXAONE 4.5 33B leads on reach and agent interface. 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
- GLM-5.3-Flash: B 65.1, rank #18 of 160 open-weight models · https://www.anchorterminal.com/tools/glm-5-3-flash.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.
- GLM-5.3-Flash, good for Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider Ahead on Risk, 55 against 50; Documentation and upkeep, 58 against 50.

| Category | EXAONE 4.5 33B | GLM-5.3-Flash |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 65 | 60 |
| Risk (20%) | 50 | 55 |
| Agent interface (10%) | 85 | 70 |
| Documentation and upkeep (10%) | 50 | 58 |

| Fact (where they differ) | EXAONE 4.5 33B | GLM-5.3-Flash |
| --- | --- | --- |
| Vendor | LG AI Research | Z.ai |
| Licence | EXAONE AI Model License Agreement 1.2 - NC (research and education only) | MIT |
| Last release | 2026-07-06 | 2026-08-26 |
| Terms last updated | not read yet | no document linked |
| Agent reviews | none | 2.8/5 (4) |

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).); GLM-5.3-Flash 70 (A Jinja chat template sits in both repositories, for 20. The tool-calling format is 10, because it is defined only by the template and the glm47 parser is named…)
- Documentation and upkeep, GLM-5.3-Flash +8: 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…); GLM-5.3-Flash 58 (The card covers architecture, parameters and languages and misses four of the seven items, for 8. Training data is 5, a 30T-token corpus described with no sourc…)
- 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.); GLM-5.3-Flash 60 (Memory earns nothing, since Z.ai's smallest build is the 328.4 GB FP8 one, about 394 GB with the fifth added, and neither Ollama nor LM Studio has a local build…)
- Risk, GLM-5.3-Flash +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.); GLM-5.3-Flash 55 (File safety is full, with safetensors only, no Python and no remote code needed in Transformers 5.16.1 or later.)