# OTel-2.0-LLM-31B-IT vs Baidu Unlimited-OCR-2 (slim)

> OTel-2.0-LLM-31B-IT scores 56.8 (C) to Baidu Unlimited-OCR-2's 54.2 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-unlimited-ocr-2.md (~10,650 tokens) · this version ~830 tokens · JSON https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-unlimited-ocr-2.json · canonical https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-unlimited-ocr-2
- 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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OTel-2.0-LLM-31B-IT scores 56.8 (C) on agent readiness against Baidu Unlimited-OCR-2's 54.2 (C), and leads in 2 of 4 scored categories. Baidu Unlimited-OCR-2 leads on reach and agent interface. Both do open-weight models you run yourself.

- 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
- Baidu Unlimited-OCR-2: C 54.2, rank #133 of 160 open-weight models · https://www.anchorterminal.com/tools/unlimited-ocr-2.min.md
- 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 45; Documentation and upkeep, 74 against 50.
- Baidu Unlimited-OCR-2, good for Pipelines with an NVIDIA GPU that need long scanned or rendered documents parsed in one pass, with page order, cross-page tables and boxes kept Ahead on Reach, 55 against 50; Agent interface, 75 against 60.

| Category | OTel-2.0-LLM-31B-IT | Baidu Unlimited-OCR-2 |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 50 | 55 |
| Risk (20%) | 55 | 45 |
| Agent interface (10%) | 60 | 75 |
| Documentation and upkeep (10%) | 74 | 50 |

| Fact (where they differ) | OTel-2.0-LLM-31B-IT | Baidu Unlimited-OCR-2 |
| --- | --- | --- |
| Vendor | AT&T (Open Telco AI) | Baidu |
| Licence | Apache-2.0 | MIT (Hub metadata; the Hugging Face repository has no LICENSE file, and the GitHub code repository's LICENSE is the MIT text, copyright 2026 Baidu) |
| Last release | 2026-11-01 | 2026-10-26 |

Why the edge:
- Documentation and upkeep, OTel-2.0-LLM-31B-IT +24: 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.); Baidu Unlimited-OCR-2 50 (The card covers intended use and the new attention, and misses limitations, parameters, context length, languages and a cut-off (5).)
- Agent interface, Baidu Unlimited-OCR-2 +15: OTel-2.0-LLM-31B-IT 60 (Chat template 20, chat_template.jinja in the repository, 19,300 characters, the publisher's own.); Baidu Unlimited-OCR-2 75 (Read for a document parser, the tool-calling, trained tool calling and structured-output lines stand for what it takes and returns.)
- Risk, OTel-2.0-LLM-31B-IT +10: OTel-2.0-LLM-31B-IT 55 (File safety 25, safetensors only, with no .bin, .pt or .pth files and no Python files.); Baidu Unlimited-OCR-2 45 (File safety gets 15, safetensors only but loaded with trust_remote_code or through a prebuilt SGLang wheel from the repository.)
- Reach, Baidu Unlimited-OCR-2 +5: 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…); Baidu Unlimited-OCR-2 55 (Memory earns the full 30, since the only build is 6.69 GB of BF16 weights that need about 8 GB with the fifth added.)