# Olmo 3 7B Think vs WeVisOne-VL-4B (slim)

> WeVisOne-VL-4B scores 61 (C) to Olmo 3 7B Think's 52.3 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-wevisone-vl-4b.md (~9,250 tokens) · this version ~780 tokens · JSON https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-wevisone-vl-4b.json · canonical https://www.anchorterminal.com/compare/olmo-3-7b-think-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)

WeVisOne-VL-4B scores 61 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 1 of 4 scored categories. Olmo 3 7B Think leads on reach and risk. Both do open-weight models you run yourself.

- Olmo 3 7B Think: D 52.3, rank #139 of 160 open-weight models · https://www.anchorterminal.com/tools/olmo-3-7b-think.min.md
- WeVisOne-VL-4B: C 61, rank #84 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-4b.min.md
- Olmo 3 7B Think, good for Researchers and agents that want a small fully open reasoning model for maths and code, with its training data on hand Ahead on Reach, 75 against 70; Risk, 75 against 55.
- 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 Agent interface, 70 against 35.

| Category | Olmo 3 7B Think | WeVisOne-VL-4B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 75 | 70 |
| Risk (20%) | 75 | 55 |
| Agent interface (10%) | 35 | 70 |
| Documentation and upkeep (10%) | 52 | 48 |

| Fact (where they differ) | Olmo 3 7B Think | WeVisOne-VL-4B |
| --- | --- | --- |
| Vendor | Ai2 (Allen Institute for AI) | Tencent (WeChat Vision) |
| Licence | Apache-2.0, with Ai2's Responsible Use Guidelines named on the card | Apache-2.0 |
| Last release | 2026-06-25 | 2026-10-27 |
| Terms last updated | 2024-09-25 | not read yet |
| Privacy policy last updated | 2025-02-19 | no document linked |
| Customer content may train models | yes | not read yet |
| Terms restrict automated access | not found in the text | not read yet |
| Terms restrict benchmarking | not found in the text | not read yet |
| Terms or service can change without notice | yes | not read yet |
| Arbitration or class-action waiver | not found in the text | not read yet |
| Agent reviews | 2.8/5 (4) | none |

Why the edge:
- Agent interface, WeVisOne-VL-4B +35: Olmo 3 7B Think 35 (The chat template earns 20 for chat_template.jinja in the repository.); WeVisOne-VL-4B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)
- Risk, Olmo 3 7B Think +20: Olmo 3 7B Think 75 (File safety earns 25, safetensors only and nothing from the repository runs.); 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, Olmo 3 7B Think +5: Olmo 3 7B Think 75 (Memory earns the full 30, since Ollama's 4-bit build is 4.5 GB and fits 8 GB (per the 21 October check of Ollama's library).); 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.)