# Cola2 vs Qwen3.5-122B-A10B (slim)

> Qwen3.5-122B-A10B scores 63.1 (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-qwen3-5-122b-a10b.md (~9,050 tokens) · this version ~780 tokens · JSON https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-122b-a10b.json · canonical https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-122b-a10b
- 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)

Qwen3.5-122B-A10B scores 63.1 (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
- Qwen3.5-122B-A10B: B 63.1, rank #25 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-5-122b-a10b.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.
- Qwen3.5-122B-A10B, good for Agents with a 128 GB unified-memory machine or two large GPUs that want a capable vision-language model with a plain licence. Ahead on Reach, 80 against 25; Risk, 55 against 40; Agent interface, 70 against 20.

| Category | Cola2 | Qwen3.5-122B-A10B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 25 | 80 |
| Risk (20%) | 40 | 55 |
| Agent interface (10%) | 20 | 70 |
| Documentation and upkeep (10%) | 58 | 34 |

| Fact (where they differ) | Cola2 | Qwen3.5-122B-A10B |
| --- | --- | --- |
| 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 | 2026-02-24 |
| Agent reviews | none | 3/5 (4) |

Why the edge:
- Reach, Qwen3.5-122B-A10B +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.); Qwen3.5-122B-A10B 80 (Memory earns 10 of 30, since Alibaba's GPTQ 4-bit build is 78.9 GB, about 95 GB with a fifth added, which fits our reference machine but no smaller band.)
- Agent interface, Qwen3.5-122B-A10B +50: Cola2 20 (Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer.); Qwen3.5-122B-A10B 70 (The template ships in the repository (20) and renders several tool calls per turn for a model trained for tool use (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…); Qwen3.5-122B-A10B 34 (The card covers five of the seven items and misses limitations and a cut-off (14).)
- Risk, Qwen3.5-122B-A10B +15: Cola2 40 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).); Qwen3.5-122B-A10B 55 (Safetensors only with no repository code run on load or to build prompts gives file safety 25, and files in the Qwen organisation the publisher's GitHub links t…)