# Spark-X2.6-4B vs WeVisOne-VL-2B (slim)

> Spark-X2.6-4B and WeVisOne-VL-2B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. 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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Spark-X2.6-4B and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-2B leads on risk. Both do open-weight models you run yourself.

- Spark-X2.6-4B: C 61, rank #56 of 160 open-weight models · https://www.anchorterminal.com/tools/spark-x2-6-4b.min.md
- WeVisOne-VL-2B: C 61, rank #83 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-2b.min.md
- Spark-X2.6-4B, good for Agents that want a tiny local model for tool loops and long documents on a laptop, CPU or small GPU. Ahead on Reach, 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 Risk, 55 against 50.

| Category | Spark-X2.6-4B | WeVisOne-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 85 | 70 |
| Risk (20%) | 50 | 55 |
| Agent interface (10%) | 70 | 70 |
| Documentation and upkeep (10%) | 51 | 48 |

| Fact (where they differ) | Spark-X2.6-4B | WeVisOne-VL-2B |
| --- | --- | --- |
| Vendor | SparkLLM (XHToken) | Tencent (WeChat Vision) |
| Last release | 2026-10-20 | 2026-10-28 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 3/5 (4) | none |

Why the edge:
- Reach, Spark-X2.6-4B +15: Spark-X2.6-4B 85 (Memory 30, SparkLLM's Q4_K_M GGUF is 3.19 GB, about 3.8 GB with a fifth added, which fits 8 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.)
- Risk, WeVisOne-VL-2B +5: Spark-X2.6-4B 50 (File safety 15 of 25, safetensors and GGUF only, but the card's Transformers and vLLM routes run the repository's Python.); 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.)