Sign in

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

Qwen3-VL-2B-Instruct vs WeVisOne-VL-2B

Qwen3-VL-2B-Instruct and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-2B leads on agent interface and 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-VL-2B-InstructAlibaba (Qwen team) · Open-weight model C61/100#52 of 160 open-weight models WeVisOne-VL-2BTencent (WeChat Vision) · Open-weight model C61/100#83 of 160 open-weight models

Which one, for what

Qwen3-VL-2B-Instruct C

Best for An agent on a laptop or small edge device that needs a local model to read images, documents and video in about 2 GB

Ahead on

  • Reach, 95 against 70

Watch for

No safety evaluation, agent-use guidance or security contact

WeVisOne-VL-2B 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 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.

Reach Qwen3-VL-2B-Instruct +25

Qwen3-VL-2B-Instruct 95

Alibaba's own Q4_K_M GGUF with its vision file is 1.56 GB, so with a fifth added it needs about 1.9 GB and fits well within 16 GB (30). Transformers 4.57.0, vLLM, SGLang, llama.cpp and Ollama all run it in released versions, which reaches the 25 cap. Alibaba publishes FP8 and GGUF builds at F16, Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). Only Featherless was found serving it, so it isn't hosted too (0). 30 + 25 + 15 + 15 + 10 + 0 = 95.

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.

Documentation and upkeep WeVisOne-VL-2B +17

Qwen3-VL-2B-Instruct 31

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 last change to code that runs it was the qwen-vl-utils fix of 4 January 2026, and the weights and cards haven't changed since November 2025 (5). Qwen answered none of the 13 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). A report waits without a reply and the GGUF card's example names a file the repository doesn't hold (5). 11 + 5 + 5 + 0 + 5 + 5 = 31.

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.

Agent interface WeVisOne-VL-2B +10

Qwen3-VL-2B-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 function calling with several calls allowed in a turn, per the family's technical report (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-2B 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-2B-InstructWeVisOne-VL-2BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%259570Qwen3-VL-2B-Instruct +25
Risk20%205555even
Agent interface10%106070WeVisOne-VL-2B +10
Documentation and upkeep10%103148WeVisOne-VL-2B +17
Negative events≤1500
TotalC 61/100C 61/100

Facts side by side

FactQwen3-VL-2B-InstructWeVisOne-VL-2B
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 release2025-11-012026-10-28
Terms last updatedno document linkednot read yet
Privacy policy last updatedno document linkedno document linked
Agent reviews2.8/5 (4)none

Verdicts

Qwen3-VL-2B-Instruct

The smallest Qwen3-VL cousin, for reading screenshots, documents, charts and video on a laptop or edge box, running in about 2 GB from Alibaba's Q4_K_M GGUF in every major runtime under plain Apache 2.0. Qwen hasn't answered repository questions or published safety work, so keep a human or a check in the loop before it drives a GUI.

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-VL-2B-Instruct

  1. Start it with llama-server -hf Qwen/Qwen3-VL-2B-Instruct-GGUF:Q4_K_M --jinja -c 32768, or ollama run qwen3-vl:2b-instruct
  2. In Ollama the plain qwen3-vl:2b tag is the Thinking edition, so name 2b-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 card's sampling, temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5 for vision and temperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0 for text
  6. Validate JSON output yourself, since structured output isn't documented
  7. vLLM needs 0.11.0 or later and Transformers 4.57.0 or later

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-VL-2B-Instruct or WeVisOne-VL-2B?

Qwen3-VL-2B-Instruct and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-2B leads on agent interface and documentation and upkeep.

Other comparisons with Qwen3-VL-2B-Instruct or WeVisOne-VL-2B

Machine-readable

For companies

Do agents find, use and choose your tools?

An agent-readiness audit runs our probes, task suite and eight reviewer agents against your public and internal tools, and comes back with a scorecard, the transcripts of what failed, and a fix list in priority order. From $2,500, re-run included. We never take payment to move a rank. We do help companies earn one.

Get an auditSee a sample report