# Cola2 vs GLM-4.7-Flash (slim)

> GLM-4.7-Flash scores 63 (B) to Cola2's 33.9 (F) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/cola2-vs-glm-4-7-flash.md (~8,750 tokens) · this version ~780 tokens · JSON https://www.anchorterminal.com/compare/cola2-vs-glm-4-7-flash.json · canonical https://www.anchorterminal.com/compare/cola2-vs-glm-4-7-flash
- 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)

GLM-4.7-Flash scores 63 (B) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep. Both do open-weight models you run yourself.

- Cola2: F 33.9, rank #160 of 160 open-weight models · https://www.anchorterminal.com/tools/cola2.min.md
- GLM-4.7-Flash: B 63, rank #26 of 160 open-weight models · https://www.anchorterminal.com/tools/glm-4-7-flash.min.md
- Cola2, good for Researchers working on latent or diffusion language models who want a checkpoint to study Ahead on Documentation and upkeep, 58 against 28.
- GLM-4.7-Flash, good for Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime Ahead on Reach, 80 against 25; Risk, 55 against 40; Agent interface, 85 against 20.

| Category | Cola2 | GLM-4.7-Flash |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 25 | 80 |
| Risk (20%) | 40 | 55 |
| Agent interface (10%) | 20 | 85 |
| Documentation and upkeep (10%) | 58 | 28 |

| Fact (where they differ) | Cola2 | GLM-4.7-Flash |
| --- | --- | --- |
| Vendor | ByteDance (Seed) | Z.ai (Zhipu AI) |
| Licence | None stated for the weights (the code is Apache 2.0) | MIT |
| Last release | 2026-11-06 | 2026-01-29 |
| Agent reviews | none | 2.8/5 (4) |

Why the edge:
- Agent interface, GLM-4.7-Flash +65: Cola2 20 (Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer.); GLM-4.7-Flash 85 (A Jinja chat template sits in the repository, for 20. The tool-calling format is the full 25, because Z.ai itself names the glm47 parser for vLLM and SGLang, sa…)
- Reach, GLM-4.7-Flash +55: Cola2 25 (Memory 15, the only build is the F32 safetensors set, 35.07 GB, and 35.07 GB × 1.2 = 42.1 GB, within 64 GB.); GLM-4.7-Flash 80 (Memory earns 25, since Ollama's smallest build is the 19 GB q4_K_M one, about 22.8 GB with the fifth added, which fits 24 GB.)
- Documentation and upkeep, Cola2 +30: Cola2 58 (Model card 8, Intended use and limitations (the out-of-scope list, and the unconverged latent model) and the architecture are on the card and the GitHub model c…); GLM-4.7-Flash 28 (The card covers architecture, parameters and languages and misses four of the seven items, for 8. Training data is 0, as the card describes none.)
- Risk, GLM-4.7-Flash +15: Cola2 40 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).); GLM-4.7-Flash 55 (File safety is full, with safetensors only and the card loading it through Transformers' own code without remote code.)