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

> Qwen3.8-Flash-Next and WeVisOne-VL-2B score within a point of each other for open-weight models you run yourself, 61.8 and 61 out of 100. 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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Qwen3.8-Flash-Next and WeVisOne-VL-2B score within a point of each other on agent readiness, 61.8 (C) and 61 (C). WeVisOne-VL-2B leads on reach, risk and documentation and upkeep. Both do open-weight models you run yourself.

- Qwen3.8-Flash-Next: C 61.8, rank #34 of 160 open-weight models · https://www.anchorterminal.com/tools/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
- Qwen3.8-Flash-Next, good for Teams with a multi-GPU server or a 128 GB Mac who want Qwen's newest sparse architecture for agentic coding and multimodal work, and aren't a hosted-model or coding-assistant business.
- 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; Documentation and upkeep, 48 against 36.

| Category | Qwen3.8-Flash-Next | WeVisOne-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 65 | 70 |
| Risk (20%) | 45 | 55 |
| Agent interface (10%) | 70 | 70 |
| Documentation and upkeep (10%) | 36 | 48 |

| Fact (where they differ) | Qwen3.8-Flash-Next | WeVisOne-VL-2B |
| --- | --- | --- |
| Vendor | Alibaba (Qwen team) | Tencent (WeChat Vision) |
| Licence | Qwen Community License 1.0 (custom, not an OSI licence). Free of charge, the model name shown in products above 100M monthly users or US$20M monthly revenue, and a separate licence from Qwen before commercial use by a Model as a Service or AI Work Assistant business | Apache-2.0 |
| Last release | 2026-08-31 | 2026-10-28 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 2/5 (4) | none |

Why the edge:
- Documentation and upkeep, WeVisOne-VL-2B +12: Qwen3.8-Flash-Next 36 (Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, languages and a knowledge cut-off aren't.); WeVisOne-VL-2B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Risk, WeVisOne-VL-2B +10: Qwen3.8-Flash-Next 45 (File safety 25, safetensors only and nothing from the repository runs to load it or build prompts.); 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.)
- Reach, WeVisOne-VL-2B +5: Qwen3.8-Flash-Next 65 (Memory 5 of 30, the smallest listed 4-bit build is Ollama's 120 GB q4_K_M, 144 GB with a fifth added, which is beyond the reference machine and inside 256 GB.); 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.)