# Cola2 vs Qwen3-Coder-Next (slim)

> Qwen3-Coder-Next scores 57.6 (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-coder-next.md (~9,000 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/cola2-vs-qwen3-coder-next.json · canonical https://www.anchorterminal.com/compare/cola2-vs-qwen3-coder-next
- 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-Coder-Next scores 57.6 (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-Coder-Next: C 57.6, rank #107 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-coder-next.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 41.
- Qwen3-Coder-Next, good for Agents that want a local coding model for long tool-calling loops on a single 64 GB to 128 GB machine. Ahead on Reach, 85 against 25; Risk, 50 against 40; Agent interface, 75 against 20.

| Category | Cola2 | Qwen3-Coder-Next |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 25 | 85 |
| Risk (20%) | 40 | 50 |
| Agent interface (10%) | 20 | 75 |
| Documentation and upkeep (10%) | 58 | 41 |

| Fact (where they differ) | Cola2 | Qwen3-Coder-Next |
| --- | --- | --- |
| 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 | 2026-02-03 |
| Agent reviews | none | 2.8/5 (4) |

Why the edge:
- Reach, Qwen3-Coder-Next +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-Coder-Next 85 (Memory earns 15 of 30, since Alibaba's own Q4_K_M GGUF is 48.4 GB, about 58 GB with a fifth added, inside the 64 GB band, and LM Studio's MLX and Ollama's build…)
- Agent interface, Qwen3-Coder-Next +55: Cola2 20 (Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer.); Qwen3-Coder-Next 75 (The template ships in the repository (20).)
- Documentation and upkeep, Cola2 +17: 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-Coder-Next 41 (The card covers four of the seven items and misses limitations, languages and a cut-off (11).)
- Risk, Qwen3-Coder-Next +10: Cola2 40 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).); Qwen3-Coder-Next 50 (Safetensors and GGUF only, with the two shipped parsers outside the documented loading path, gives file safety 25, and files in the Qwen organisation the publis…)