# DeepSeek-V4-Flash-0731 vs OTel-2.0-LLM-31B-IT (slim)

> DeepSeek-V4-Flash-0731 scores 61.9 (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-v4-flash-0731-vs-otel-2-0-llm-31b-it.md (~10,800 tokens) · this version ~830 tokens · JSON https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-otel-2-0-llm-31b-it.json · canonical https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-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-V4-Flash-0731 scores 61.9 (C) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 2 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-V4-Flash-0731: C 61.9, rank #33 of 160 open-weight models · https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.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-V4-Flash-0731, good for Agents or teams with a multi-GPU server, or a provider account, who want DeepSeek's V4 agent model under MIT with a 1M context Ahead on Reach, 70 against 50; Agent interface, 75 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 Risk, 55 against 50; Documentation and upkeep, 74 against 38.

| Category | DeepSeek-V4-Flash-0731 | OTel-2.0-LLM-31B-IT |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 70 | 50 |
| Risk (20%) | 50 | 55 |
| Agent interface (10%) | 75 | 60 |
| Documentation and upkeep (10%) | 38 | 74 |

| Fact (where they differ) | DeepSeek-V4-Flash-0731 | OTel-2.0-LLM-31B-IT |
| --- | --- | --- |
| Vendor | DeepSeek | AT&T (Open Telco AI) |
| Licence | MIT | Apache-2.0 |
| Last release | 2026-07-31 | 2026-11-01 |
| Agent reviews | 2.5/5 (4) | none |

Why the edge:
- Documentation and upkeep, OTel-2.0-LLM-31B-IT +36: DeepSeek-V4-Flash-0731 38 (Model card 8, the card and the DSpark card it names give architecture, parameters and context, with intended use, limitations, languages and cut-off missing.); 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-V4-Flash-0731 +20: DeepSeek-V4-Flash-0731 70 (Memory 5, because the smallest build of 4-bit or better from a runtime's own library is LM Studio's MXFP4 GGUF at 156.4 GB, which with a fifth added needs 187.7…); 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, DeepSeek-V4-Flash-0731 +15: DeepSeek-V4-Flash-0731 75 (Chat template 10, there is no Jinja template and the format is given as Python to run.); OTel-2.0-LLM-31B-IT 60 (Chat template 20, chat_template.jinja in the repository, 19,300 characters, the publisher's own.)
- Risk, OTel-2.0-LLM-31B-IT +5: DeepSeek-V4-Flash-0731 50 (File safety 15, safetensors only, less 10 because prompts are built by running DeepSeek's encoding_dsv4.py from the repository and the card's serving commands p…); OTel-2.0-LLM-31B-IT 55 (File safety 25, safetensors only, with no .bin, .pt or .pth files and no Python files.)