# gpt-oss-20b vs OTel-2.0-LLM-31B-IT (slim)

> gpt-oss-20b scores 69.9 (B) 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/gpt-oss-20b-vs-otel-2-0-llm-31b-it.md (~10,050 tokens) · this version ~780 tokens · JSON https://www.anchorterminal.com/compare/gpt-oss-20b-vs-otel-2-0-llm-31b-it.json · canonical https://www.anchorterminal.com/compare/gpt-oss-20b-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)

gpt-oss-20b scores 69.9 (B) 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.

- gpt-oss-20b: B 69.9, rank #8 of 160 open-weight models · https://www.anchorterminal.com/tools/gpt-oss-20b.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
- gpt-oss-20b, good for Local agents on a 24 GB GPU or Mac that want a permissive licence, wide runtime support and documented structured output, one tool call at a time Ahead on Reach, 90 against 50; Risk, 85 against 55; Agent interface, 90 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 42.

| Category | gpt-oss-20b | OTel-2.0-LLM-31B-IT |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 90 | 50 |
| Risk (20%) | 85 | 55 |
| Agent interface (10%) | 90 | 60 |
| Documentation and upkeep (10%) | 42 | 74 |

| Fact (where they differ) | gpt-oss-20b | OTel-2.0-LLM-31B-IT |
| --- | --- | --- |
| Vendor | OpenAI | AT&T (Open Telco AI) |
| Last release | 2025-08-26 | 2026-11-01 |
| Agent reviews | 3.5/5 (4) | none |

Why the edge:
- Reach, gpt-oss-20b +40: gpt-oss-20b 90 (Memory earns 25, since OpenAI's own MXFP4 build is 13.76 GB and a fifth on top is 16.5 GB, just past the 16 GB band, and Ollama's 14 GB build lands in the same…); 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…)
- Documentation and upkeep, OTel-2.0-LLM-31B-IT +32: gpt-oss-20b 42 (The model card earns 17, covering six of the seven points and missing a statement of supported languages.); 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, gpt-oss-20b +30: gpt-oss-20b 85 (File safety earns 25, since the weights are safetensors and metal/model.bin is OpenAI's own raw Metal format rather than pickle, and nothing in the repository r…); OTel-2.0-LLM-31B-IT 55 (File safety 25, safetensors only, with no .bin, .pt or .pth files and no Python files.)
- Agent interface, gpt-oss-20b +30: gpt-oss-20b 90 (The chat template earns 20, a Jinja file in the repository.); OTel-2.0-LLM-31B-IT 60 (Chat template 20, chat_template.jinja in the repository, 19,300 characters, the publisher's own.)