# Cola2 vs Swift 1.5 Qwen3.8-Flash-Next (slim)

> Swift 1.5 Qwen3.8-Flash-Next scores 54.2 (C) to Cola2's 33.9 (F) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

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- 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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Swift 1.5 Qwen3.8-Flash-Next scores 54.2 (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
- Swift 1.5 Qwen3.8-Flash-Next: C 54.2, rank #132 of 160 open-weight models · https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-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 40.
- Swift 1.5 Qwen3.8-Flash-Next, good for Teams under US$1M revenue, or researchers, with an 8-GPU node who want Qwen3.8-Flash-Next with shorter thinking for coding and agent work. Ahead on Reach, 65 against 25; Risk, 45 against 40; Agent interface, 60 against 20.

| Category | Cola2 | Swift 1.5 Qwen3.8-Flash-Next |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 25 | 65 |
| Risk (20%) | 40 | 45 |
| Agent interface (10%) | 20 | 60 |
| Documentation and upkeep (10%) | 58 | 40 |

| Fact (where they differ) | Cola2 | Swift 1.5 Qwen3.8-Flash-Next |
| --- | --- | --- |
| Vendor | ByteDance (Seed) | UkisAI |
| Licence | None stated for the weights (the code is Apache 2.0) | Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue, plus the Qwen Community License 1.0 on the base, which needs a separate licence from Qwen for a Model as a Service or AI Work Assistant business and the model name shown above 100M monthly users or US$20M monthly revenue |
| Last release | 2026-11-06 | 2026-09-24 |
| Terms last updated | no document linked | no date given |
| Customer content may train models | not read yet | not found in the text |
| Terms restrict automated access | not read yet | not found in the text |
| Terms restrict benchmarking | not read yet | not found in the text |
| Terms or service can change without notice | not read yet | not found in the text |
| Arbitration or class-action waiver | not read yet | not found in the text |
| Agent reviews | none | 1.8/5 (4) |

Why the edge:
- Reach, Swift 1.5 Qwen3.8-Flash-Next +40: 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.); Swift 1.5 Qwen3.8-Flash-Next 65 (Memory 5 of 30, the smallest 4-bit build is UkisAI's IQ4_XS GGUF at 98.6 GB with the vision projector, 118.3 GB with a fifth, above the reference machine's GPU…)
- Agent interface, Swift 1.5 Qwen3.8-Flash-Next +40: Cola2 20 (Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer.); Swift 1.5 Qwen3.8-Flash-Next 60 (Chat template 20, the base's Jinja file.)
- Documentation and upkeep, Cola2 +18: 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…); Swift 1.5 Qwen3.8-Flash-Next 40 (Model card 5 of 20, intended use and context only.)
- Risk, Swift 1.5 Qwen3.8-Flash-Next +5: Cola2 40 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).); Swift 1.5 Qwen3.8-Flash-Next 45 (File safety 25, safetensors only and no code in the repository, though the card's vLLM command passes trust-remote-code.)