Head to head · Open-weight models you run yourself · October 2026 research run
WeVisOne-VL-2B vs WeVisOne-VL-4B
WeVisOne-VL-2B and WeVisOne-VL-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons
WeVisOne-VL-2BTencent (WeChat Vision) · Open-weight model
C61/100#83 of 160 open-weight models
WeVisOne-VL-4BTencent (WeChat Vision) · Open-weight model
C61/100#84 of 160 open-weight models
Which one, for what
Best for Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
The pixel decoder ships as a pickle file
Best for Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
The pixel decoder ships as a pickle file
Score by category
| Category | Weight this run | WeVisOne-VL-2B | WeVisOne-VL-4B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 70 | 70 | even |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 70 | 70 | even |
| Documentation and upkeep | 10%10 | 48 | 48 | even |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | WeVisOne-VL-2B | WeVisOne-VL-4B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Tencent (WeChat Vision) | Tencent (WeChat Vision) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-28 | 2026-10-27 |
| Privacy policy last updated | no document linked | no document linked |
Verdicts
WeVisOne-VL-2B
A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results.
WeVisOne-VL-4B
A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results.
Before you call either
WeVisOne-VL-2B
- Use Tencent's package:
WeVisOneVL("tencent/WeVisOne-VL-2B", backend="vllm")thenmodel.predict(image, task=..., query=...) - Tasks are detection, referring, point, count_box, count_point, ocr, ocr_grounding, segmentation and matting
- A None answer means the target isn't in the image. Treat it as a result, not an error
- mask_decoder.pt is a pickle. Load it only from the tencent repository, and check its SHA-256 against the Hub's
- Send one image a turn. Video grids are rejected by the decoder
- The card says it isn't for identifying individuals or for high-impact decisions on its own
WeVisOne-VL-4B
- Use Tencent's package:
WeVisOneVL("tencent/WeVisOne-VL-4B", backend="vllm")thenmodel.predict(image, task=..., query=...) - Tasks are detection, referring, point, count_box, count_point, ocr, ocr_grounding, segmentation and matting
- A None answer means the target isn't in the image. Treat it as a result, not an error
- mask_decoder.pt is a pickle. Load it only from the tencent repository, and check its SHA-256 against the Hub's
- Send one image a turn. Video grids are rejected by the decoder
- The card says it isn't for identifying individuals or for high-impact decisions on its own
Questions
Which is better for AI agents, WeVisOne-VL-2B or WeVisOne-VL-4B?
WeVisOne-VL-2B and WeVisOne-VL-4B score within a point of each other on agent readiness, 61 (C) and 61 (C).
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Machine-readable
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/compare/wevisone-vl-2b-vs-wevisone-vl-4b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/wevisone-vl-2b.json·/api/v1/tools/wevisone-vl-4b.json - From a terminal
anchor compare wevisone-vl-2b wevisone-vl-4b(the CLI) - Over MCP
compare_tools {"a": "wevisone-vl-2b", "b": "wevisone-vl-4b"}at/mcp, no key