# gpt-oss-20b vs WeVisOne-VL-4B (slim)

> gpt-oss-20b scores 69.9 (B) to WeVisOne-VL-4B's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-wevisone-vl-4b.md (~9,500 tokens) · this version ~780 tokens · JSON https://www.anchorterminal.com/compare/gpt-oss-20b-vs-wevisone-vl-4b.json · canonical https://www.anchorterminal.com/compare/gpt-oss-20b-vs-wevisone-vl-4b
- 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)

gpt-oss-20b scores 69.9 (B) on agent readiness against WeVisOne-VL-4B's 61 (C), and leads in 3 of 4 scored categories. WeVisOne-VL-4B leads on documentation and upkeep. Both do open-weight models you run yourself.

- gpt-oss-20b: B 69.9, rank #8 of 160 open-weight models · https://www.anchorterminal.com/tools/gpt-oss-20b.min.md
- WeVisOne-VL-4B: C 61, rank #84 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-4b.min.md
- gpt-oss-20b, good for Local agents on a 24 GB GPU or Mac that want a permissive licence, wide runtime support and documented structured output, one tool call at a time Ahead on Reach, 90 against 70; Risk, 85 against 55; Agent interface, 90 against 70.
- WeVisOne-VL-4B, good for Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence Ahead on Documentation and upkeep, 48 against 42.

| Category | gpt-oss-20b | WeVisOne-VL-4B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 90 | 70 |
| Risk (20%) | 85 | 55 |
| Agent interface (10%) | 90 | 70 |
| Documentation and upkeep (10%) | 42 | 48 |

| Fact (where they differ) | gpt-oss-20b | WeVisOne-VL-4B |
| --- | --- | --- |
| Vendor | OpenAI | Tencent (WeChat Vision) |
| Last release | 2025-08-26 | 2026-10-27 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 3.5/5 (4) | none |

Why the edge:
- Risk, gpt-oss-20b +30: gpt-oss-20b 85 (File safety earns 25, since the weights are safetensors and metal/model.bin is OpenAI's own raw Metal format rather than pickle, and nothing in the repository r…); WeVisOne-VL-4B 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, gpt-oss-20b +20: gpt-oss-20b 90 (Memory earns 25, since OpenAI's own MXFP4 build is 13.76 GB and a fifth on top is 16.5 GB, just past the 16 GB band, and Ollama's 14 GB build lands in the same…); WeVisOne-VL-4B 70 (Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 9.7 GB, and 9.7 GB × 1.2 = 11.6 GB, within 16 GB.)
- Agent interface, gpt-oss-20b +20: gpt-oss-20b 90 (The chat template earns 20, a Jinja file in the repository.); WeVisOne-VL-4B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)
- Documentation and upkeep, WeVisOne-VL-4B +6: gpt-oss-20b 42 (The model card earns 17, covering six of the seven points and missing a statement of supported languages.); WeVisOne-VL-4B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)