# Qwen3.5-122B-A10B vs WeVisOne-VL-2B

> Qwen3.5-122B-A10B scores 63.1 (B) to WeVisOne-VL-2B's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-wevisone-vl-2b
- Markdown: https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-wevisone-vl-2b.md (~9,050 tokens)
- Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- DNS: dig +short TXT <slug>.grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.<category>. and <a>.vs.<b>. under the zone, and the same under grade.letme.dev)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-11-10

Qwen3.5-122B-A10B scores 63.1 (B) on agent readiness against WeVisOne-VL-2B's 61 (C), and leads in 1 of 4 scored categories. WeVisOne-VL-2B leads on documentation and upkeep. Both do open-weight models you run yourself.

- Qwen3.5-122B-A10B: grade B, 63.1/100, rank #25 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-122b-a10b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-122b-a10b.json
- WeVisOne-VL-2B: grade C, 61/100, rank #83 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/wevisone-vl-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/wevisone-vl-2b.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Qwen3.5-122B-A10B (B)

Good for: Agents with a 128 GB unified-memory machine or two large GPUs that want a capable vision-language model with a plain licence.

Ahead on:
- Reach, 80 against 70

Watch for: Too big for a single 64 GB machine even at 4 bits

### WeVisOne-VL-2B (C)

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 34

Watch for: The pixel decoder ships as a pickle file


## Why the edge

### Documentation and upkeep: WeVisOne-VL-2B +14

- Qwen3.5-122B-A10B (34): The card covers five of the seven items and misses limitations and a cut-off (14). Training data is a general description without a cut-off (5). The files last changed on 24 April 2026, 192 days before this check, past the 180-day band and within a year (5), and Qwen's GitHub repository holds only a README and licence, so no reference code of Alibaba's changed later. No Qwen member has answered any of the 21 discussions that aren't pull requests, recounted on 2 November (0), and GitHub issues couldn't be counted. It's a first release at this size (5), and a template fix and a run report still wait unanswered (5). 14 + 5 + 5 + 0 + 5 + 5 = 34.
- WeVisOne-VL-2B (48): Model card 8, Intended use, limitations and the architecture are on the card. Training data 5, A general description in the README of a data engine and over 100 million annotated images, with no sources named and no cut-off. Last change 20, the last commit, on 28 October 2026, put the weights back in BF16, 13 days before this check. Responsiveness 0, the one discussion that isn't a pull request (23 October 2026) has no reply from a tencent member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release. File health 10, Config, template and card agree on the Qwen3-VL classes, and the card names mask_decoder.pt as required and it's there. 8 + 5 + 20 + 0 + 5 + 10 = 48.

### Reach: Qwen3.5-122B-A10B +10

- Qwen3.5-122B-A10B (80): Memory earns 10 of 30, since Alibaba's GPTQ 4-bit build is 78.9 GB, about 95 GB with a fifth added, which fits our reference machine but no smaller band. Runtimes reach the 25 cap on released llama.cpp, Ollama, vLLM, SGLang and Transformers. Alibaba's FP8 and GPTQ-Int4 builds give builds the full 15. Apache 2.0 with nothing added gives terms 15, the ungated repositories give download 10, and Alibaba's own Model Studio plus Novita and DeepInfra give hosted too its 5.
- 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. No runtime library has a build. Runtimes 15, the architecture is Qwen3VLForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors file, three at 5 each. Builds 0, Tencent publishes the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata with the LICENSE file in the repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 30 + 15 + 0 + 15 + 10 + 0 = 70.

## Score by category

| Category | Weight | Qwen3.5-122B-A10B | WeVisOne-VL-2B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 80 | 70 | Qwen3.5-122B-A10B +10 |
| Risk | 20% (20 this run) | 55 | 55 | even |
| Agent interface | 10% (10 this run) | 70 | 70 | even |
| Documentation and upkeep | 10% (10 this run) | 34 | 48 | WeVisOne-VL-2B +14 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **63.1 · B** | **61 · C** | |

## Facts side by side

| Fact | Qwen3.5-122B-A10B | WeVisOne-VL-2B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Alibaba (Qwen team) | 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 | 2026-02-24 | 2026-10-28 |
| Terms last updated | no document linked | not read yet |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 3/5 (4) | none |

## How the scores have moved

### Qwen3.5-122B-A10B

- -0.5 on 2 November 2026: time

### WeVisOne-VL-2B

No score change since it was listed.

## Verdicts

**Qwen3.5-122B-A10B.** A strong mid-size cousin for coding, agent steps and reading images or video, with thinking you can switch off per request. Alibaba's 4-bit build needs about 95 GB, which fits one 128 GB unified-memory machine, and several providers host it. The main risk is silence, with no safety results, agent guidance or answers from Alibaba.

**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.

## Before you call either

### Qwen3.5-122B-A10B

1. Quickest start on a 128 GB machine is `ollama run qwen3.5:122b` (Q4_K_M, 81 GB of files, about 97 GB with context)
2. Alibaba's 4-bit build serves with `vllm serve Qwen/Qwen3.5-122B-A10B-GPTQ-Int4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --quantization moe_wna16`, though that kernel isn't documented for AMD gfx1151
3. Use the qwen3_coder tool parser and the qwen3 reasoning parser, as the card's commands do
4. Turn thinking off with `"chat_template_kwargs": {"enable_thinking": false}` in the request body, since it's on by default and `/think` and `/nothink` aren't supported
5. Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without
6. Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens
7. Add `--language-model-only` in vLLM to skip the vision encoder and free memory for the KV cache when you only need text
8. Validate any JSON it returns, since structured output isn't documented

### WeVisOne-VL-2B

1. Use Tencent's package: `WeVisOneVL("tencent/WeVisOne-VL-2B", backend="vllm")` then `model.predict(image, task=..., query=...)`
2. Tasks are detection, referring, point, count_box, count_point, ocr, ocr_grounding, segmentation and matting
3. A None answer means the target isn't in the image. Treat it as a result, not an error
4. mask_decoder.pt is a pickle. Load it only from the tencent repository, and check its SHA-256 against the Hub's
5. Send one image a turn. Video grids are rejected by the decoder
6. The card says it isn't for identifying individuals or for high-impact decisions on its own

## Questions

### Which is better for AI agents, Qwen3.5-122B-A10B or WeVisOne-VL-2B?

Qwen3.5-122B-A10B scores 63.1 (B) on agent readiness against WeVisOne-VL-2B's 61 (C), and leads in 1 of 4 scored categories. WeVisOne-VL-2B leads on documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-wevisone-vl-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-wevisone-vl-2b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "qwen3-5-122b-a10b", "b": "wevisone-vl-2b"}`. From a terminal: `anchor compare qwen3-5-122b-a10b wevisone-vl-2b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/qwen3-5-122b-a10b.json and https://www.anchorterminal.com/api/v1/tools/wevisone-vl-2b.json

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