# gpt-oss-120b vs Nanbeige4.2-3B (slim)

> gpt-oss-120b scores 70.2 (BB) to Nanbeige4.2-3B'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/gpt-oss-120b-vs-nanbeige-4-2-3b.md (~9,650 tokens) · this version ~780 tokens · JSON https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nanbeige-4-2-3b.json · canonical https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nanbeige-4-2-3b
- 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 Nanbeige4.2-3B's 61 (C), and leads in 2 of 4 scored categories. Nanbeige4.2-3B leads on reach and documentation and upkeep. Both do open-weight models you run yourself.

- gpt-oss-120b: BB 70.2, rank #6 of 160 open-weight models · https://www.anchorterminal.com/tools/gpt-oss-120b.min.md
- Nanbeige4.2-3B: C 61, rank #78 of 160 open-weight models · https://www.anchorterminal.com/tools/nanbeige-4-2-3b.min.md
- 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 Risk, 95 against 55; Agent interface, 90 against 85. Also Agent-ready, a grade of BB or better.
- Nanbeige4.2-3B, good for Local agents that need tool calling and a long context on a laptop or a small GPU Ahead on Reach, 85 against 75; Documentation and upkeep, 72 against 45.

| Category | gpt-oss-120b | Nanbeige4.2-3B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 75 | 85 |
| Risk (20%) | 95 | 55 |
| Agent interface (10%) | 90 | 85 |
| Documentation and upkeep (10%) | 45 | 72 |

| Fact (where they differ) | gpt-oss-120b | Nanbeige4.2-3B |
| --- | --- | --- |
| Vendor | OpenAI | Nanbeige LLM Lab (BOSS Zhipin) |
| Licence | Apache-2.0 | Apache-2.0 (Hub metadata only; the repository has no LICENSE file) |
| Last release | 2025-08-26 | 2026-09-11 |
| Agent reviews | 3.5/5 (4) | none |

Why the edge:
- Risk, gpt-oss-120b +40: 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.); Nanbeige4.2-3B 55 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).)
- Documentation and upkeep, Nanbeige4.2-3B +27: gpt-oss-120b 45 (The model card earns 20, with all seven items in OpenAI's arXiv card.); Nanbeige4.2-3B 72 (Model card 17, Intended use, limitations, the architecture, the parameters (3B outside the embeddings), the context and the languages (in the metadata) are on t…)
- Reach, Nanbeige4.2-3B +10: 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…); Nanbeige4.2-3B 85 (Memory 30, the smallest build from Nanbeige is the FP8 repository (the 8-bit GPTQ one is the same size), 5.19 GB, and 5.19 GB × 1.2 = 6.2 GB, within 16 GB.)
- Agent interface, gpt-oss-120b +5: gpt-oss-120b 90 (The chat template earns 20, a Jinja file in the repository.); Nanbeige4.2-3B 85 (Chat template 20, A Jinja chat template in tokenizer_config.json (7,961 characters), with enable_thinking, preserve_thinking, tools and tool_call_format switche…)