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Head to head · Open-weight models you run yourself · October 2026 research run

Qwen3.6-35B-A3B vs WeVisOne-VL-4B

Qwen3.6-35B-A3B scores 62.7 (B) on agent readiness against WeVisOne-VL-4B's 61 (C), and leads in 1 of 4 scored categories. WeVisOne-VL-4B leads on documentation and upkeep. 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

Qwen3.6-35B-A3BAlibaba (Qwen team) · Open-weight model B62.7/100#27 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

Qwen3.6-35B-A3B B

Best for Agents with a 32 GB or larger machine that want a quick, permissively licensed coding and tool-calling cousin with vision.

Ahead on

  • Reach, 85 against 70

Watch for

No safety evaluation, no agent-use guidance and no way to report a security flaw

WeVisOne-VL-4B C

Best 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 31

Watch for

The pixel decoder ships as a pickle file

Why the edge

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

Documentation and upkeep WeVisOne-VL-4B +17

Qwen3.6-35B-A3B 31

The model card earns 11, covering intended use, architecture, parameters and context but not limitations, languages or a cut-off. Training data earns 5 for a general description with no cut-off. The last change earns 5, a README edit on 24 April 2026, 192 days before this check, past 180 days and within a year, and Qwen's GitHub repository holds only a README and licence, so no reference code of Alibaba's changed later. Responsiveness earns 0, with no Qwen reply in the 40 most recent of 62 discussions that aren't pull requests, recounted on 2 November. Version policy earns 5, the earlier version kept in its own repository without a note. File health earns 5, the files agree but reports about tool calling and the template wait unanswered.

WeVisOne-VL-4B 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 27 October 2026, added links to the paper and project page, 14 days before this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (23 and 26 October 2026) has a 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, 14 days old. 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 Qwen3.6-35B-A3B +15

Qwen3.6-35B-A3B 85

Memory earns 20 because the smallest 4-bit build from a runtime's own library is Ollama's Q4_K_M at 23 GB, which comes to 27.6 GB with the fifth added, so it needs a 32 GB machine rather than a 24 GB one. Runtimes earn the full 25, since released llama.cpp, Ollama, vLLM, SGLang and Transformers all run the architecture. Builds earn 10 for Alibaba's one quantised build, FP8, with no 4-bit or GGUF from the publisher. Terms earn 15 for plain Apache 2.0, download earns 10 for ungated files, and hosted earns 5 because DeepInfra, Featherless AI and Scaleway serve it through Hugging Face.

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. 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

CategoryWeight this runQwen3.6-35B-A3BWeVisOne-VL-4BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%258570Qwen3.6-35B-A3B +15
Risk20%205555even
Agent interface10%107070even
Documentation and upkeep10%103148WeVisOne-VL-4B +17
Negative events≤1500
TotalB 62.7/100C 61/100

Facts side by side

FactQwen3.6-35B-A3BWeVisOne-VL-4B
KindOpen-weight modelOpen-weight model
VendorAlibaba (Qwen team)Tencent (WeChat Vision)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-04-162026-10-27
Terms last updatedno document linkednot read yet
Privacy policy last updatedno document linkedno document linked
Agent reviews2.8/5 (4)none

How the scores have moved

Qwen3.6-35B-A3B

WeVisOne-VL-4B

No score change since it was listed.

Verdicts

Qwen3.6-35B-A3B

A fast mixture-of-experts cousin with 3B active parameters for coding and tool calling, with image and video input, in every major runtime under plain Apache 2.0. Ollama's 4-bit build needs about 28 GB. The main risk is that nobody at Alibaba maintains it, with no safety results, no security contact and no replies to tool-calling reports.

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

Qwen3.6-35B-A3B

  1. Start it with ollama run qwen3.6:35b (Q4_K_M, 23 to 24 GB on disk, about 28 GB in memory)
  2. On the reference machine use vllm serve Qwen/Qwen3.6-35B-A3B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, BF16, since vLLM doesn't document FP8 compute on gfx1151
  3. Use the qwen3_coder tool-call parser and the qwen3 reasoning parser in vLLM 0.19.0 or SGLang 0.5.10 or later
  4. Thinking is on by default and /nothink isn't supported, so pass enable_thinking as false in chat_template_kwargs for short tasks, or preserve_thinking as true to keep earlier reasoning
  5. Card sampling for thinking is temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5, and for non-thinking temperature 0.7, top_p 0.8
  6. Keep at least 128K of context as the card advises, and turn on YaRN only beyond 262,144 tokens
  7. Validate JSON output yourself or use the runtime's guided decoding, since the model card documents none

WeVisOne-VL-4B

  1. Use Tencent's package: WeVisOneVL("tencent/WeVisOne-VL-4B", 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, Qwen3.6-35B-A3B or WeVisOne-VL-4B?

Qwen3.6-35B-A3B scores 62.7 (B) on agent readiness against WeVisOne-VL-4B's 61 (C), and leads in 1 of 4 scored categories. WeVisOne-VL-4B leads on documentation and upkeep.

Other comparisons with Qwen3.6-35B-A3B or WeVisOne-VL-4B

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