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

Qwen3-VL-30B-A3B-Instruct vs WeVisOne-VL-4B

WeVisOne-VL-4B scores 61 (C) on agent readiness against Qwen3-VL-30B-A3B-Instruct's 58 (C), and leads in 2 of 4 scored categories. Qwen3-VL-30B-A3B-Instruct leads on reach. 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-VL-30B-A3B-InstructAlibaba (Qwen team) · Open-weight model C58/100#104 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-VL-30B-A3B-Instruct C

Best for An agent with 24 GB or more to spare that wants a local reader of images, documents and video with 262,144 tokens of context

Ahead on

  • Reach, 95 against 70

Watch for

No safety evaluation, agent-use guidance or security contact

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

  • Agent interface, 70 against 60
  • Documentation and upkeep, 48 against 36

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.

Reach Qwen3-VL-30B-A3B-Instruct +25

Qwen3-VL-30B-A3B-Instruct 95

Alibaba's own Q4_K_M GGUF is 18.6 GB, so with a fifth added it needs about 22 GB, or 23 GB with the vision file, and fits 24 GB (25). Transformers 4.57.0, vLLM 0.11.0, SGLang 0.5.4, llama.cpp and Ollama all run it in released versions, which reaches the 25 cap. Alibaba publishes FP8 and GGUF builds at Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). Novita, Featherless AI and DeepInfra serve it on Hugging Face, and SiliconFlow too on OpenRouter (5). 25 + 25 + 15 + 15 + 10 + 5 = 95.

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.

Documentation and upkeep WeVisOne-VL-4B +12

Qwen3-VL-30B-A3B-Instruct 36

The card misses limitations, languages and a cut-off (11). Training data is described only in the technical report, with no cut-off (5). The newest Hub commit is the library-tag commit of 26 November 2025 and the weights haven't changed since September 2025, so the qwen-vl-utils fix of 4 January 2026 in the code that runs it sets the line, within a year (5). Qwen answered none of the eight Hugging Face discussions that aren't pull requests (0), and GitHub issues weren't counted. Earlier versions kept in their own repositories without notes (5). Config, template and card agree and no broken-file report waits (10). 11 + 5 + 5 + 0 + 5 + 10 = 36.

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.

Agent interface WeVisOne-VL-4B +10

Qwen3-VL-30B-A3B-Instruct 60

A Jinja chat template in the repository (20). The tool-call format lives only in the template, Hermes-style JSON in tool_call tags, and Alibaba names no parser (10). Trained for multimodal function calling, with several calls allowed in a turn (20). No documented structured output (0). No thinking mode in this edition, so reasoning control scores 0 by the rule. 262,144 tokens of native context (10).

WeVisOne-VL-4B 70

Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template. Tool-calling format 25, Read for a perception model as what it takes and returns. Trained tool calling 10, Read for a perception model as the inputs it was trained to use. Structured output 15, Read for a perception model as typed outputs. Reasoning control 0, the card says it writes structured output directly with no chain-of-thought reasoning. Context 0, Tencent states no context length for it. 20 + 25 + 10 + 15 + 0 + 0 = 70.

Score by category

CategoryWeight this runQwen3-VL-30B-A3B-InstructWeVisOne-VL-4BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%259570Qwen3-VL-30B-A3B-Instruct +25
Risk20%205555even
Agent interface10%106070WeVisOne-VL-4B +10
Documentation and upkeep10%103648WeVisOne-VL-4B +12
Negative events≤1500
TotalC 58/100C 61/100

Facts side by side

FactQwen3-VL-30B-A3B-InstructWeVisOne-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 release2025-11-012026-10-27
Terms last updatedno document linkednot read yet
Privacy policy last updatedno document linkedno document linked
Agent reviews3/5 (4)none

Verdicts

Qwen3-VL-30B-A3B-Instruct

A vision-language mixture of experts for screenshots, documents, charts and video, with about 3B of its 31B parameters active. Alibaba's Q4_K_M GGUF with its vision file needs about 23 GB, it loads in Transformers, vLLM, SGLang, llama.cpp and Ollama under Apache 2.0, and three providers host it. No safety work is published, so add guardrails before it drives a GUI.

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-VL-30B-A3B-Instruct

  1. Start it with llama-server -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M --jinja -c 32768, or ollama run qwen3-vl:30b-a3b-instruct
  2. In Ollama the plain qwen3-vl:30b tag is the Thinking edition, so name 30b-a3b-instruct
  3. With the GGUF, load the mmproj vision file too, or images won't be read
  4. Parse tool calls as JSON inside <tool_call> tags, or use SGLang's --tool-call-parser qwen
  5. Use the shipped sampling, temperature 0.7, top_p 0.8 and top_k 20, from generation_config.json
  6. Validate JSON output yourself, since structured output isn't documented
  7. vLLM needs 0.11.0 or later, Transformers 4.57.0 or later and llama.cpp b6907 or later

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-VL-30B-A3B-Instruct or WeVisOne-VL-4B?

WeVisOne-VL-4B scores 61 (C) on agent readiness against Qwen3-VL-30B-A3B-Instruct's 58 (C), and leads in 2 of 4 scored categories. Qwen3-VL-30B-A3B-Instruct leads on reach.

Other comparisons with Qwen3-VL-30B-A3B-Instruct or WeVisOne-VL-4B

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