# Ornith-1.5-397B vs WeVisOne-VL-2B (slim)

> WeVisOne-VL-2B scores 61 (C) to Ornith-1.5-397B's 59 (C) 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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WeVisOne-VL-2B scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories. Ornith-1.5-397B leads on agent interface. Both do open-weight models you run yourself.

- Ornith-1.5-397B: C 59, rank #100 of 160 open-weight models · https://www.anchorterminal.com/tools/ornith-1-5-397b.min.md
- WeVisOne-VL-2B: C 61, rank #83 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-2b.min.md
- Ornith-1.5-397B, good for A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required Ahead on Agent interface, 85 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 Reach, 70 against 65; Risk, 55 against 50; Documentation and upkeep, 48 against 36.

| Category | Ornith-1.5-397B | WeVisOne-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 65 | 70 |
| Risk (20%) | 50 | 55 |
| Agent interface (10%) | 85 | 70 |
| Documentation and upkeep (10%) | 36 | 48 |

| Fact (where they differ) | Ornith-1.5-397B | WeVisOne-VL-2B |
| --- | --- | --- |
| Vendor | Ornith | Tencent (WeChat Vision) |
| Licence | MIT | Apache-2.0 |
| Last release | 2026-08-23 | 2026-10-28 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 2/5 (4) | none |

Why the edge:
- Agent interface, Ornith-1.5-397B +15: Ornith-1.5-397B 85 (Chat template 20, a Jinja file in the repository.); 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 +12: Ornith-1.5-397B 36 (Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off.); WeVisOne-VL-2B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Reach, WeVisOne-VL-2B +5: Ornith-1.5-397B 65 (Memory 0, since the smallest 4-bit build, Ollama's at 242 GB, needs about 290 GB with the fifth added, above the 256 GB band, and Ornith's own Q4_K_M GGUF and N…); 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.)
- Risk, WeVisOne-VL-2B +5: Ornith-1.5-397B 50 (File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories for this size, so the card's --trust-remote-code flag loads nothing from…); 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.)