# EXAONE 4.5 33B vs gpt-oss-120b (slim)

> gpt-oss-120b scores 70.2 (BB) 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-gpt-oss-120b.md (~9,600 tokens) · this version ~780 tokens · JSON https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-gpt-oss-120b.json · canonical https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-gpt-oss-120b
- 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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gpt-oss-120b scores 70.2 (BB) on agent readiness against EXAONE 4.5 33B's 61 (C), and leads in 3 of 4 scored categories. EXAONE 4.5 33B leads on 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
- gpt-oss-120b: BB 70.2, rank #6 of 160 open-weight models · https://www.anchorterminal.com/tools/gpt-oss-120b.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 Documentation and upkeep, 50 against 45.
- gpt-oss-120b, good for Agents on one 80 GB GPU or a 128 GB unified machine that want an Apache 2.0 reasoning model with wide runtime support Ahead on Reach, 75 against 65; Risk, 95 against 50; Agent interface, 90 against 85. Also Agent-ready, a grade of BB or better.

| Category | EXAONE 4.5 33B | gpt-oss-120b |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 65 | 75 |
| Risk (20%) | 50 | 95 |
| Agent interface (10%) | 85 | 90 |
| Documentation and upkeep (10%) | 50 | 45 |

| Fact (where they differ) | EXAONE 4.5 33B | gpt-oss-120b |
| --- | --- | --- |
| Vendor | LG AI Research | OpenAI |
| Licence | EXAONE AI Model License Agreement 1.2 - NC (research and education only) | Apache-2.0 |
| Last release | 2026-07-06 | 2025-08-26 |
| Terms last updated | not read yet | no document linked |
| Agent reviews | none | 3.5/5 (4) |

Why the edge:
- Risk, gpt-oss-120b +45: EXAONE 4.5 33B 50 (File safety 25, safetensors and GGUF only, no .bin, .pt or .pth files and no Python in the repository.); gpt-oss-120b 95 (File safety earns 25, safetensors only, with metal/model.bin a raw format and not pickle, and nothing from the repository runs on load.)
- Reach, gpt-oss-120b +10: 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.); gpt-oss-120b 75 (Memory earns 10, because the smallest 4-bit build, Ollama's 65 GB gpt-oss:120b, needs about 78 GB with the fifth added, which fits the reference machine but not…)
- Agent interface, gpt-oss-120b +5: EXAONE 4.5 33B 85 (Chat template 20, chat_template.jinja in the repository (5,930 characters).); gpt-oss-120b 90 (The chat template earns 20, a Jinja file in the repository.)
- Documentation and upkeep, EXAONE 4.5 33B +5: 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…); gpt-oss-120b 45 (The model card earns 20, with all seven items in OpenAI's arXiv card.)