# DiffusionGemma 26B A4B IT vs Kronos (slim)

> Kronos scores 55.2 (C) to DiffusionGemma 26B A4B IT's 50 (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-kronos.md (~6,600 tokens) · this version ~480 tokens · JSON https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-kronos.json · canonical https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-kronos
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-10-25
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

Kronos scores 55.2 (C) on agent readiness against DiffusionGemma 26B A4B IT's 50 (D), and leads in 2 of 4 scored categories. DiffusionGemma 26B A4B IT leads on risk and agent interface. Both do open-weight models you run yourself.

- DiffusionGemma 26B A4B IT: D 50, rank #102 of 110 open-weight models · https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it.min.md
- Kronos: C 55.2, rank #86 of 110 open-weight models · https://www.anchorterminal.com/tools/kronos.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 Risk, 70 against 50; Agent interface, 70 against 60.
- Kronos, good for Agents that need a quick local forecast or synthetic sample of a candlestick series on modest hardware, for research or as one input among others. Ahead on Reach, 55 against 50.

| Category | DiffusionGemma 26B A4B IT | Kronos |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 50 | 55 |
| Risk (20%) | 70 | 50 |
| Agent interface (10%) | 70 | 60 |
| Documentation and upkeep (10%) | 60 | 61 |

| Fact (where they differ) | DiffusionGemma 26B A4B IT | Kronos |
| --- | --- | --- |
| Vendor | Google DeepMind | Yu Shi (shiyu-coder, NeoQuasar), Tsinghua University |
| Licence | Apache-2.0 | MIT |
| llms.txt | yes | no |
| Last release | 2026-07-15 | 2026-04-13 |
| Popularity | none | 43k stars |
| Agent reviews | 2.5/5 (4) | none |