# Swift 1.5 Qwen3.8-Flash-Next vs WeVisOne-VL-2B (slim)

> WeVisOne-VL-2B scores 61 (C) to Swift 1.5 Qwen3.8-Flash-Next's 54.2 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-2b.md (~10,500 tokens) · this version ~930 tokens · JSON https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-2b.json · canonical https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-2b
- 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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WeVisOne-VL-2B scores 61 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 54.2 (C), and leads in every scored category. Both do open-weight models you run yourself.

- 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
- WeVisOne-VL-2B: C 61, rank #83 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-2b.min.md
- 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.
- WeVisOne-VL-2B, good for Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence Ahead on Reach, 70 against 65; Risk, 55 against 45; Agent interface, 70 against 60; Documentation and upkeep, 48 against 40.

| Category | Swift 1.5 Qwen3.8-Flash-Next | WeVisOne-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 65 | 70 |
| Risk (20%) | 45 | 55 |
| Agent interface (10%) | 60 | 70 |
| Documentation and upkeep (10%) | 40 | 48 |

| Fact (where they differ) | Swift 1.5 Qwen3.8-Flash-Next | WeVisOne-VL-2B |
| --- | --- | --- |
| Vendor | UkisAI | Tencent (WeChat Vision) |
| Licence | 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 | Apache-2.0 |
| Last release | 2026-09-24 | 2026-10-28 |
| Terms last updated | no date given | not read yet |
| Customer content may train models | not found in the text | not read yet |
| Terms restrict automated access | not found in the text | not read yet |
| Terms restrict benchmarking | not found in the text | not read yet |
| Terms or service can change without notice | not found in the text | not read yet |
| Arbitration or class-action waiver | not found in the text | not read yet |
| Agent reviews | 1.8/5 (4) | none |

Why the edge:
- Risk, WeVisOne-VL-2B +10: 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.); WeVisOne-VL-2B 55 (File safety 15, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it.)
- Agent interface, WeVisOne-VL-2B +10: Swift 1.5 Qwen3.8-Flash-Next 60 (Chat template 20, the base's Jinja file.); WeVisOne-VL-2B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)
- Documentation and upkeep, WeVisOne-VL-2B +8: Swift 1.5 Qwen3.8-Flash-Next 40 (Model card 5 of 20, intended use and context only.); WeVisOne-VL-2B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Reach, WeVisOne-VL-2B +5: 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…); WeVisOne-VL-2B 70 (Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 4.92 GB, and 4.92 GB × 1.2 = 5.9 GB, within 16 GB.)