# Nanbeige4.3-33B vs OTel-2.0-LLM-31B-IT (slim)

> Nanbeige4.3-33B scores 60.8 (C) to OTel-2.0-LLM-31B-IT's 56.8 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-otel-2-0-llm-31b-it.md (~10,150 tokens) · this version ~780 tokens · JSON https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-otel-2-0-llm-31b-it.json · canonical https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-otel-2-0-llm-31b-it
- 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
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

Nanbeige4.3-33B scores 60.8 (C) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 3 of 4 scored categories. OTel-2.0-LLM-31B-IT leads on documentation and upkeep. Both do open-weight models you run yourself.

- Nanbeige4.3-33B: C 60.8, rank #86 of 160 open-weight models · https://www.anchorterminal.com/tools/nanbeige-4-3-33b.min.md
- OTel-2.0-LLM-31B-IT: C 56.8, rank #111 of 160 open-weight models · https://www.anchorterminal.com/tools/otel-2-0-llm-31b-it.min.md
- Nanbeige4.3-33B, good for Agents with a 32 GB GPU (INT4) or more, or the 128 GB reference machine at BF16, that want an Apache-tagged 33B model for long coding and tool loops. Ahead on Reach, 80 against 50; Risk, 60 against 55; Agent interface, 85 against 60.
- OTel-2.0-LLM-31B-IT, good for Telecoms teams that want an open model for standards questions and RAG on their own hardware Ahead on Documentation and upkeep, 74 against 56.

| Category | Nanbeige4.3-33B | OTel-2.0-LLM-31B-IT |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 80 | 50 |
| Risk (20%) | 60 | 55 |
| Agent interface (10%) | 85 | 60 |
| Documentation and upkeep (10%) | 56 | 74 |

| Fact (where they differ) | Nanbeige4.3-33B | OTel-2.0-LLM-31B-IT |
| --- | --- | --- |
| Vendor | Nanbeige LLM Lab (BOSS Zhipin) | AT&T (Open Telco AI) |
| Licence | Apache-2.0 (Hub metadata only; the repository has no LICENSE file) | Apache-2.0 |
| Last release | 2026-10-31 | 2026-11-01 |

Why the edge:
- Reach, Nanbeige4.3-33B +30: Nanbeige4.3-33B 80 (Memory earns 20, since Nanbeige's INT4 build is 22.57 GB, about 27 GB with a fifth added, which fits 32 GB.); OTel-2.0-LLM-31B-IT 50 (Memory 10, the publisher's only build is the BF16 safetensors, 62.55 GB for the 15 shards the index names, and 62.55 GB × 1.2 = 75.1 GB, above 64 GB and inside…)
- Agent interface, Nanbeige4.3-33B +25: Nanbeige4.3-33B 85 (The chat template earns 20, Jinja in tokenizer_config.json.); OTel-2.0-LLM-31B-IT 60 (Chat template 20, chat_template.jinja in the repository, 19,300 characters, the publisher's own.)
- Documentation and upkeep, OTel-2.0-LLM-31B-IT +18: Nanbeige4.3-33B 56 (The model card earns 11, with intended use, parameters, context and languages but no limitations, architecture or cut-off.); OTel-2.0-LLM-31B-IT 74 (Model card 14, Intended use, a long limitations section, the architecture and training method, the parameters (31B) and the language (English) are on the card.)
- Risk, Nanbeige4.3-33B +5: Nanbeige4.3-33B 60 (File safety earns 25, safetensors only and no repository code, so the card's trust_remote_code has nothing to run.); OTel-2.0-LLM-31B-IT 55 (File safety 25, safetensors only, with no .bin, .pt or .pth files and no Python files.)