# Qwen3-Coder-Next vs WeVisOne-VL-2B (slim)

> WeVisOne-VL-2B scores 61 (C) to Qwen3-Coder-Next's 57.6 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/qwen3-coder-next-vs-wevisone-vl-2b.md (~9,650 tokens) · this version ~780 tokens · JSON https://www.anchorterminal.com/compare/qwen3-coder-next-vs-wevisone-vl-2b.json · canonical https://www.anchorterminal.com/compare/qwen3-coder-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 Qwen3-Coder-Next's 57.6 (C), and leads in 2 of 4 scored categories. Qwen3-Coder-Next leads on reach and agent interface. Both do open-weight models you run yourself.

- Qwen3-Coder-Next: C 57.6, rank #107 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-coder-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-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 70; Agent interface, 75 against 70.
- 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 Risk, 55 against 50; Documentation and upkeep, 48 against 41.

| Category | Qwen3-Coder-Next | WeVisOne-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 85 | 70 |
| Risk (20%) | 50 | 55 |
| Agent interface (10%) | 75 | 70 |
| Documentation and upkeep (10%) | 41 | 48 |

| Fact (where they differ) | Qwen3-Coder-Next | WeVisOne-VL-2B |
| --- | --- | --- |
| Vendor | Alibaba (Qwen team) | Tencent (WeChat Vision) |
| Last release | 2026-02-03 | 2026-10-28 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 2.8/5 (4) | none |

Why the edge:
- Reach, Qwen3-Coder-Next +15: 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…); 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.)
- Documentation and upkeep, WeVisOne-VL-2B +7: Qwen3-Coder-Next 41 (The card covers four of the seven items and misses limitations, languages and a cut-off (11).); WeVisOne-VL-2B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Risk, WeVisOne-VL-2B +5: 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…); 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, Qwen3-Coder-Next +5: Qwen3-Coder-Next 75 (The template ships in the repository (20).); WeVisOne-VL-2B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)