# EXAONE 4.5 33B vs Qwen3-4B-Instruct-2507 (slim)

> EXAONE 4.5 33B scores 61 (C) to Qwen3-4B-Instruct-2507's 54.5 (C) for open-weight models you run yourself. 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 scores 61 (C) on agent readiness against Qwen3-4B-Instruct-2507's 54.5 (C), and leads in 2 of 4 scored categories. Qwen3-4B-Instruct-2507 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
- Qwen3-4B-Instruct-2507: C 54.5, rank #130 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507.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 Agent interface, 85 against 75; Documentation and upkeep, 50 against 26.
- Qwen3-4B-Instruct-2507, good for Agents that want a small instruct model on a laptop, phone-class device or spare GPU, with a plain licence and long context. Ahead on Reach, 90 against 65; Risk, 55 against 50.

| Category | EXAONE 4.5 33B | Qwen3-4B-Instruct-2507 |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 65 | 90 |
| Risk (20%) | 50 | 55 |
| Agent interface (10%) | 85 | 75 |
| Documentation and upkeep (10%) | 50 | 26 |

| Fact (where they differ) | EXAONE 4.5 33B | Qwen3-4B-Instruct-2507 |
| --- | --- | --- |
| Vendor | LG AI Research | Alibaba (Qwen team) |
| Licence | EXAONE AI Model License Agreement 1.2 - NC (research and education only) | Apache-2.0 |
| Last release | 2026-07-06 | 2025-09-17 |
| Terms last updated | not read yet | no document linked |
| Agent reviews | none | 3/5 (4) |

Why the edge:
- Reach, Qwen3-4B-Instruct-2507 +25: 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.); Qwen3-4B-Instruct-2507 90 (Memory 30, LM Studio's and Ollama's 4-bit builds are 2.3 to 2.5 GB, about 3 GB with a fifth added, inside 16 GB.)
- Documentation and upkeep, EXAONE 4.5 33B +24: 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…); Qwen3-4B-Instruct-2507 26 (Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, named languages and a knowledge cut-off aren't.)
- Agent interface, EXAONE 4.5 33B +10: EXAONE 4.5 33B 85 (Chat template 20, chat_template.jinja in the repository (5,930 characters).); Qwen3-4B-Instruct-2507 75 (Chat template 20, a Jinja template in tokenizer_config.json.)
- Risk, Qwen3-4B-Instruct-2507 +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.); Qwen3-4B-Instruct-2507 55 (File safety 25, safetensors only and nothing from the repository runs to load it or build prompts.)