# Cola2 vs Qwen3.5-4B (slim)

> Qwen3.5-4B scores 61.2 (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/cola2-vs-qwen3-5-4b.md (~8,550 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-4b.json · canonical https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-4b
- 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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Qwen3.5-4B scores 61.2 (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.

- Cola2: F 33.9, rank #160 of 160 open-weight models · https://www.anchorterminal.com/tools/cola2.min.md
- Qwen3.5-4B: C 61.2, rank #38 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-5-4b.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 34.
- Qwen3.5-4B, good for Agents that want a cheap local helper for tool calls, short reasoning and reading images on a laptop or small GPU Ahead on Reach, 85 against 25; Risk, 55 against 40; Agent interface, 70 against 20.

| Category | Cola2 | Qwen3.5-4B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 25 | 85 |
| Risk (20%) | 40 | 55 |
| Agent interface (10%) | 20 | 70 |
| Documentation and upkeep (10%) | 58 | 34 |

| Fact (where they differ) | Cola2 | Qwen3.5-4B |
| --- | --- | --- |
| Vendor | ByteDance (Seed) | Alibaba (Qwen team) |
| Licence | None stated for the weights (the code is Apache 2.0) | Apache-2.0 |
| Last release | 2026-11-06 | 2026-03-02 |
| Agent reviews | none | 3/5 (4) |

Why the edge:
- Reach, Qwen3.5-4B +60: 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.); Qwen3.5-4B 85 (Memory earns the full 30 because Ollama's own Q4_K_M build is 3.3 GB, about 4 GB with the fifth added, though Alibaba itself publishes no quantised 4B, so build…)
- Agent interface, Qwen3.5-4B +50: Cola2 20 (Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer.); Qwen3.5-4B 70 (The template ships in the repository (20) and the model was trained for tool use with several calls per turn in the template (20).)
- Documentation and upkeep, Cola2 +24: 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…); Qwen3.5-4B 34 (The card covers five of the seven items and misses limitations and a cut-off (14).)
- Risk, Qwen3.5-4B +15: Cola2 40 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).); Qwen3.5-4B 55 (Safetensors only with no remote code gives file safety its 25, and files in the Qwen organisation the publisher's GitHub links to, each with a hash on the Hub…)