# EXAONE 4.5 33B vs Qwen3.5-122B-A10B (slim)

> Qwen3.5-122B-A10B scores 63.1 (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-qwen3-5-122b-a10b.md (~9,950 tokens) · this version ~830 tokens · JSON https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-122b-a10b.json · canonical https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-122b-a10b
- 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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Qwen3.5-122B-A10B scores 63.1 (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 agent interface and documentation and upkeep. 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
- Qwen3.5-122B-A10B: B 63.1, rank #25 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-5-122b-a10b.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 70; Documentation and upkeep, 50 against 34.
- Qwen3.5-122B-A10B, good for Agents with a 128 GB unified-memory machine or two large GPUs that want a capable vision-language model with a plain licence. Ahead on Reach, 80 against 65; Risk, 55 against 50.

| Category | EXAONE 4.5 33B | Qwen3.5-122B-A10B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 65 | 80 |
| Risk (20%) | 50 | 55 |
| Agent interface (10%) | 85 | 70 |
| Documentation and upkeep (10%) | 50 | 34 |

| Fact (where they differ) | EXAONE 4.5 33B | Qwen3.5-122B-A10B |
| --- | --- | --- |
| 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 | 2026-02-24 |
| Terms last updated | not read yet | no document linked |
| Agent reviews | none | 3/5 (4) |

Why the edge:
- Documentation and upkeep, EXAONE 4.5 33B +16: 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…); Qwen3.5-122B-A10B 34 (The card covers five of the seven items and misses limitations and a cut-off (14).)
- Reach, Qwen3.5-122B-A10B +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.); Qwen3.5-122B-A10B 80 (Memory earns 10 of 30, since Alibaba's GPTQ 4-bit build is 78.9 GB, about 95 GB with a fifth added, which fits our reference machine but no smaller band.)
- Agent interface, EXAONE 4.5 33B +15: EXAONE 4.5 33B 85 (Chat template 20, chat_template.jinja in the repository (5,930 characters).); Qwen3.5-122B-A10B 70 (The template ships in the repository (20) and renders several tool calls per turn for a model trained for tool use (20).)
- Risk, Qwen3.5-122B-A10B +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.); Qwen3.5-122B-A10B 55 (Safetensors only with no repository code run on load or to build prompts gives file safety 25, and files in the Qwen organisation the publisher's GitHub links t…)