# EXAONE 4.5 33B vs Qwen3.5-2B (slim)

> EXAONE 4.5 33B scores 61 (C) to Qwen3.5-2B's 50.5 (D) 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-qwen3-5-2b.md (~9,400 tokens) · this version ~830 tokens · JSON https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-2b.json · canonical https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-2b
- 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 Qwen3.5-2B's 50.5 (D), and leads in 2 of 4 scored categories. Qwen3.5-2B 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
- Qwen3.5-2B: D 50.5, rank #144 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-5-2b.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 60; Documentation and upkeep, 50 against 37.
- Qwen3.5-2B, good for Builders who want a cheap local helper or fine-tuning base for narrow tasks and image reading on a laptop, small GPU or CPU Ahead on Reach, 80 against 65; Risk, 55 against 50.

| Category | EXAONE 4.5 33B | Qwen3.5-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 65 | 80 |
| Risk (20%) | 50 | 55 |
| Agent interface (10%) | 85 | 60 |
| Documentation and upkeep (10%) | 50 | 37 |

| Fact (where they differ) | EXAONE 4.5 33B | Qwen3.5-2B |
| --- | --- | --- |
| 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 | 2026-03-02 |
| Terms last updated | not read yet | no document linked |
| Agent reviews | none | 3/5 (4) |

Why the edge:
- Agent interface, EXAONE 4.5 33B +25: EXAONE 4.5 33B 85 (Chat template 20, chat_template.jinja in the repository (5,930 characters).); Qwen3.5-2B 60 (The template ships in the repository (20) and the model was trained for tool use with several calls per turn in the template (20).)
- Reach, Qwen3.5-2B +15: 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.); Qwen3.5-2B 80 (Memory earns the full 30, since LM Studio's Q4_K_M GGUF with its vision projector is 1.94 GB, about 2.3 GB with the fifth added (Ollama's Q4_K_M build was 1.9 G…)
- Documentation and upkeep, EXAONE 4.5 33B +13: 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…); Qwen3.5-2B 37 (The card covers six of the seven items, missing only a cut-off (17), because it states the intended uses and warns about thinking loops.)
- Risk, Qwen3.5-2B +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.); Qwen3.5-2B 55 (Safetensors only with no remote code gives file safety its 25, and files in the Qwen organisation the publisher's GitHub links to, each with a hash on the Hub…)