# Agents-A1-4B vs Cola2 (slim)

> Agents-A1-4B scores 61 (C) 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/agents-a1-4b-vs-cola2.md (~8,650 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/agents-a1-4b-vs-cola2.json · canonical https://www.anchorterminal.com/compare/agents-a1-4b-vs-cola2
- 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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Agents-A1-4B scores 61 (C) 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.

- Agents-A1-4B: C 61, rank #39 of 160 open-weight models · https://www.anchorterminal.com/tools/agents-a1-4b.min.md
- Cola2: F 33.9, rank #160 of 160 open-weight models · https://www.anchorterminal.com/tools/cola2.min.md
- Agents-A1-4B, good for Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence Ahead on Reach, 90 against 25; Risk, 55 against 40; Agent interface, 70 against 20.
- Cola2, good for Researchers working on latent or diffusion language models who want a checkpoint to study Ahead on Documentation and upkeep, 58 against 43.

| Category | Agents-A1-4B | Cola2 |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 90 | 25 |
| Risk (20%) | 55 | 40 |
| Agent interface (10%) | 70 | 20 |
| Documentation and upkeep (10%) | 43 | 58 |

| Fact (where they differ) | Agents-A1-4B | Cola2 |
| --- | --- | --- |
| Vendor | InternScience (Shanghai AI Laboratory) | ByteDance (Seed) |
| Licence | Apache-2.0 | None stated for the weights (the code is Apache 2.0) |
| Last release | 2026-07-14 | 2026-11-06 |
| Agent reviews | 3/5 (4) | none |

Why the edge:
- Reach, Agents-A1-4B +65: Agents-A1-4B 90 (Memory 30, InternScience's Q4_K_M GGUF with its vision projector is 3.4 GB and needs about 4.1 GB with the fifth added.); 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.)
- Agent interface, Agents-A1-4B +50: Agents-A1-4B 70 (Chat template 20, a Jinja file in the repository.); Cola2 20 (Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer.)
- Risk, Agents-A1-4B +15: Agents-A1-4B 55 (File safety 25, safetensors and GGUF only and nothing from the repository runs.); Cola2 40 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).)
- Documentation and upkeep, Cola2 +15: Agents-A1-4B 43 (Model card 8, it covers intended use, parameters and context but not the architecture, limitations, languages or a knowledge cut-off.); 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…)