# InternVL3.5-GPT-OSS-20B-A4B-Preview vs WeVisOne-VL-2B (slim)

> WeVisOne-VL-2B scores 61 (C) to InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-wevisone-vl-2b.md (~10,300 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-wevisone-vl-2b.json · canonical https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-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
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

WeVisOne-VL-2B scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.

- InternVL3.5-GPT-OSS-20B-A4B-Preview: D 53.2, rank #136 of 160 open-weight models · https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview.min.md
- WeVisOne-VL-2B: C 61, rank #83 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-2b.min.md
- InternVL3.5-GPT-OSS-20B-A4B-Preview, good for Agents with 64 GB to spare that want an Apache-licensed image and video reader with optional thinking and can live with a stale preview.
- 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; Agent interface, 70 against 45; Documentation and upkeep, 48 against 28.

| Category | InternVL3.5-GPT-OSS-20B-A4B-Preview | WeVisOne-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 65 | 70 |
| Risk (20%) | 55 | 55 |
| Agent interface (10%) | 45 | 70 |
| Documentation and upkeep (10%) | 28 | 48 |

| Fact (where they differ) | InternVL3.5-GPT-OSS-20B-A4B-Preview | WeVisOne-VL-2B |
| --- | --- | --- |
| Vendor | OpenGVLab (Shanghai AI Laboratory) | Tencent (WeChat Vision) |
| Licence | Apache-2.0; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT) | Apache-2.0 |
| Last release | 2025-09-08 | 2026-10-28 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 1.8/5 (4) | none |

Why the edge:
- Agent interface, WeVisOne-VL-2B +25: InternVL3.5-GPT-OSS-20B-A4B-Preview 45 (Chat template 20, a Jinja file in the repository, though it doesn't match the tokenizer.); 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 +20: InternVL3.5-GPT-OSS-20B-A4B-Preview 28 (Model card 8 of 20, the family card misses limitations, context, languages and a cut-off.); WeVisOne-VL-2B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Reach, WeVisOne-VL-2B +5: InternVL3.5-GPT-OSS-20B-A4B-Preview 65 (Memory 15 of 30, the only builds are BF16 at 42.5 GB by the repository's safetensors index, or 51 GB with a fifth added, which fits 64 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.)