# Gemma 4 E2B IT vs WeVisOne-VL-2B (slim)

> WeVisOne-VL-2B and Gemma 4 E2B IT score within a point of each other for open-weight models you run yourself, 61 and 60.4 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-wevisone-vl-2b.md (~9,500 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-wevisone-vl-2b.json · canonical https://www.anchorterminal.com/compare/gemma-4-e2b-it-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 and Gemma 4 E2B IT score within a point of each other on agent readiness, 61 (C) and 60.4 (C). Gemma 4 E2B IT leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.

- Gemma 4 E2B IT: C 60.4, rank #88 of 160 open-weight models · https://www.anchorterminal.com/tools/gemma-4-e2b-it.min.md
- WeVisOne-VL-2B: C 61, rank #83 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-2b.min.md
- Gemma 4 E2B IT, good for Agents that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning Ahead on Reach, 95 against 70; Risk, 70 against 55; Agent interface, 85 against 70; Documentation and upkeep, 75 against 48.
- 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

| Category | Gemma 4 E2B IT | WeVisOne-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 95 | 70 |
| Risk (20%) | 70 | 55 |
| Agent interface (10%) | 85 | 70 |
| Documentation and upkeep (10%) | 75 | 48 |

| Fact (where they differ) | Gemma 4 E2B IT | WeVisOne-VL-2B |
| --- | --- | --- |
| Vendor | Google DeepMind | Tencent (WeChat Vision) |
| llms.txt | yes | no |
| Last release | 2026-11-03 | 2026-10-28 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 3.5/5 (4) | none |

Why the edge:
- Documentation and upkeep, Gemma 4 E2B IT +27: Gemma 4 E2B IT 75 (Model card 20, all seven items are there.); WeVisOne-VL-2B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Reach, Gemma 4 E2B IT +25: Gemma 4 E2B IT 95 (Memory 30, Google's own 4-bit QAT GGUF with its projector is 4.34 GB, which with a fifth on top is 5.2 GB and fits in 16 GB or less.); 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, Gemma 4 E2B IT +15: Gemma 4 E2B IT 70 (File safety 25, safetensors and GGUF only, with nothing from the repository run on load.); 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, Gemma 4 E2B IT +15: Gemma 4 E2B IT 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.)