# DiffusionGemma 26B A4B IT vs OTel-2.0-LLM-31B-IT (slim)

> OTel-2.0-LLM-31B-IT scores 56.8 (C) to DiffusionGemma 26B A4B IT's 51.3 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-otel-2-0-llm-31b-it.md (~10,700 tokens) · this version ~830 tokens · JSON https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-otel-2-0-llm-31b-it.json · canonical https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-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)

OTel-2.0-LLM-31B-IT scores 56.8 (C) on agent readiness against DiffusionGemma 26B A4B IT's 51.3 (D), and leads in 1 of 4 scored categories. DiffusionGemma 26B A4B IT leads on reach, risk and agent interface. Both do open-weight models you run yourself.

- DiffusionGemma 26B A4B IT: D 51.3, rank #143 of 160 open-weight models · https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it.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
- DiffusionGemma 26B A4B IT, good for Agents with about 64 GB of GPU memory that want fast single-user generation from text and images and can accept lower quality than Gemma 4 Ahead on Reach, 55 against 50; Risk, 70 against 55; Agent interface, 70 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 60.

| Category | DiffusionGemma 26B A4B IT | OTel-2.0-LLM-31B-IT |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 55 | 50 |
| Risk (20%) | 70 | 55 |
| Agent interface (10%) | 70 | 60 |
| Documentation and upkeep (10%) | 60 | 74 |

| Fact (where they differ) | DiffusionGemma 26B A4B IT | OTel-2.0-LLM-31B-IT |
| --- | --- | --- |
| Vendor | Google DeepMind | AT&T (Open Telco AI) |
| llms.txt | yes | no |
| Last release | 2026-07-15 | 2026-11-01 |
| Agent reviews | 2.5/5 (4) | none |

Why the edge:
- Risk, DiffusionGemma 26B A4B IT +15: DiffusionGemma 26B A4B IT 70 (File safety gets the full 25, safetensors only and loaded by Transformers' own code and a Jinja template.); OTel-2.0-LLM-31B-IT 55 (File safety 25, safetensors only, with no .bin, .pt or .pth files and no Python files.)
- Documentation and upkeep, OTel-2.0-LLM-31B-IT +14: DiffusionGemma 26B A4B IT 60 (The card covers all seven points (20), and the training data is a general description with a January 2025 cut-off (10).); 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.)
- Agent interface, DiffusionGemma 26B A4B IT +10: DiffusionGemma 26B A4B IT 70 (The chat template is in the repository (20) and handles several tool calls in one turn, and the card names function calling as trained (20).); OTel-2.0-LLM-31B-IT 60 (Chat template 20, chat_template.jinja in the repository, 19,300 characters, the publisher's own.)
- Reach, DiffusionGemma 26B A4B IT +5: DiffusionGemma 26B A4B IT 55 (Memory earns 15 because the only Google build is 51.7 GB of BF16, about 62 GB with the fifth added, which needs the 64 GB band.); 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…)