# DeepSeek-V3.2 vs OTel-2.0-LLM-31B-IT (slim)

> DeepSeek-V3.2 scores 58.6 (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/deepseek-v3-2-vs-otel-2-0-llm-31b-it.md (~9,700 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/deepseek-v3-2-vs-otel-2-0-llm-31b-it.json · canonical https://www.anchorterminal.com/compare/deepseek-v3-2-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
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DeepSeek-V3.2 scores 58.6 (C) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 1 of 4 scored categories. OTel-2.0-LLM-31B-IT leads on risk and documentation and upkeep. Both do open-weight models you run yourself.

- DeepSeek-V3.2: C 58.6, rank #102 of 160 open-weight models · https://www.anchorterminal.com/tools/deepseek-v3-2.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
- DeepSeek-V3.2, good for Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider Ahead on Reach, 60 against 50.
- 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 Risk, 55 against 50; Documentation and upkeep, 74 against 34.

| Category | DeepSeek-V3.2 | OTel-2.0-LLM-31B-IT |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 50 |
| Risk (20%) | 50 | 55 |
| Agent interface (10%) | 60 | 60 |
| Documentation and upkeep (10%) | 34 | 74 |

| Fact (where they differ) | DeepSeek-V3.2 | OTel-2.0-LLM-31B-IT |
| --- | --- | --- |
| Vendor | DeepSeek | AT&T (Open Telco AI) |
| Licence | MIT | Apache-2.0 |
| Last release | 2025-12-01 | 2026-11-01 |
| Agent reviews | 2.3/5 (4) | none |

Why the edge:
- Documentation and upkeep, OTel-2.0-LLM-31B-IT +40: DeepSeek-V3.2 34 (The model card earns 14 when read with DeepSeek's own V3.2 model card PDF, which adds intended use and a parameter count, leaving languages and a knowledge cut-…); 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.)
- Reach, DeepSeek-V3.2 +10: DeepSeek-V3.2 60 (Memory earns nothing, because the only build from DeepSeek or a runtime's library is the 689.5 GB FP8 release, which needs about 827 GB with the fifth added.); 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…)
- Risk, OTel-2.0-LLM-31B-IT +5: DeepSeek-V3.2 50 (File safety earns 15, safetensors only but 10 off because prompts are built by running DeepSeek's Python from the repository.); OTel-2.0-LLM-31B-IT 55 (File safety 25, safetensors only, with no .bin, .pt or .pth files and no Python files.)