# Qwen3.6-27B vs WeVisOne-VL-4B

> Qwen3.6-27B scores 66.7 (B) to WeVisOne-VL-4B'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/qwen3-6-27b-vs-wevisone-vl-4b
- Markdown: https://www.anchorterminal.com/compare/qwen3-6-27b-vs-wevisone-vl-4b.md (~8,750 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

Qwen3.6-27B scores 66.7 (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.

- Qwen3.6-27B: grade B, 66.7/100, rank #16 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-6-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-6-27b.json
- WeVisOne-VL-4B: grade C, 61/100, rank #84 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/wevisone-vl-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/wevisone-vl-4b.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

### Qwen3.6-27B (B)

Good for: Agents that want an open coding and tool-calling model with vision on one 24 to 32 GB GPU or Mac, with plain licence terms.

Ahead on:
- Reach, 90 against 70

Watch for: No safety results, prompt-injection guidance or security contact

### WeVisOne-VL-4B (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 31

Watch for: The pixel decoder ships as a pickle file


## Why the edge

### Reach: Qwen3.6-27B +20

- Qwen3.6-27B (90): Memory 25 of 30, Ollama's text-only 4-bit build is 17 GB and the vision build 18 GB, about 20 to 22 GB with a fifth added, which fits 24 GB. Runtimes 25, released versions of llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers all run the qwen3_5 architecture. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 15, Apache 2.0 with nothing added. Download 10, neither repository is gated. Hosted 5, three inference providers are live on Hugging Face and Alibaba's Model Studio serves it per the card.
- 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 +17

- Qwen3.6-27B (31): Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, languages and a knowledge cut-off aren't. Training data 5, only a general family description without a cut-off, in the GitHub README. Last change 5, the files last changed on 24 April 2026, 191 days before this check, and Qwen's GitHub repository holds only a README and licence, so no reference code of Alibaba's changed later. Responsiveness 0, Qwen answered none of the 40 most recent questions and reports, recounted on 1 November. Version policy 5, earlier and later 27B models stay in their own repositories without dated notes. File health 5, the files agree with the card but template and tool-calling reports wait without a reply.
- 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.

## Score by category

| Category | Weight | Qwen3.6-27B | WeVisOne-VL-4B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 90 | 70 | Qwen3.6-27B +20 |
| Risk | 20% (20 this run) | 55 | 55 | even |
| Agent interface | 10% (10 this run) | 70 | 70 | even |
| Documentation and upkeep | 10% (10 this run) | 31 | 48 | WeVisOne-VL-4B +17 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **66.7 · B** | **61 · C** | |

## Facts side by side

| Fact | Qwen3.6-27B | WeVisOne-VL-4B |
| --- | --- | --- |
| 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 | 2026-04-22 | 2026-10-27 |
| 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

### Qwen3.6-27B

- -0.5 on 1 November 2026: time

### WeVisOne-VL-4B

No score change since it was listed.

## Verdicts

**Qwen3.6-27B.** A capable dense cousin for agentic coding and tool calls with images and video in, running in every major runtime. Ollama's 4-bit build needs about 22 GB, and three providers host it. The main risk is silence, with no safety results, no agent guidance, no answers in its repository and Qwen3.8-27B now ahead of it.

**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

### Qwen3.6-27B

1. Quickest start is `ollama run qwen3.6:27b`, about 18 GB of files with the vision projector
2. Alibaba's FP8 build with the card's parsers is `vllm serve Qwen/Qwen3.6-27B-FP8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, vllm 0.19.0 or later
3. Use the qwen3_coder tool parser, the template writes calls as function and parameter blocks inside tool_call
4. Thinking is on by default, pass `"chat_template_kwargs": {"enable_thinking": false}` for short tasks, and `/think` and `/nothink` aren't supported
5. `"preserve_thinking": true` in chat_template_kwargs keeps earlier reasoning in context, which helps long agent sessions but grows the prompt
6. Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 when thinking, and temperature 0.7, top_p 0.8, presence_penalty 1.5 without
7. Keep at least 128K of context if you rely on thinking, and turn on YaRN only beyond 262,144 tokens
8. Validate any JSON it returns or use the runtime's guided decoding, since structured output isn't documented
9. Qwen3.8-27B (listed as qwen3-8-27b) is the newer model at this size on the same runtimes

### 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, Qwen3.6-27B or WeVisOne-VL-4B?

Qwen3.6-27B scores 66.7 (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.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-6-27b-vs-wevisone-vl-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-6-27b-vs-wevisone-vl-4b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "qwen3-6-27b", "b": "wevisone-vl-4b"}`. From a terminal: `anchor compare qwen3-6-27b wevisone-vl-4b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/qwen3-6-27b.json and https://www.anchorterminal.com/api/v1/tools/wevisone-vl-4b.json

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- [occamy-1.0 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-4b.md)
- [Olmo 3 7B Think vs Qwen3.6-27B](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-6-27b.md)
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- [Ornith-1.5-35B-A3B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-wevisone-vl-4b.md)
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- [Ornith-1.5-9B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-wevisone-vl-4b.md)
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- [Qwen-AgentWorld-35B-A3B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-6-27b.md)
- [Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-4b.md)
- [Qwen3-30B-A3B-Instruct-2507 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-6-27b.md)
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- [Qwen3-4B-Instruct-2507 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-6-27b.md)
- [Qwen3-4B-Instruct-2507 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-wevisone-vl-4b.md)
- [Qwen3.5-0.8B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-6-27b.md)
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- [Qwen3.5-2B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-6-27b.md)
- [Qwen3.5-2B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-wevisone-vl-4b.md)
- [Qwen3.5-397B-A17B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-6-27b.md)
- [Qwen3.5-397B-A17B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-wevisone-vl-4b.md)
- [Qwen3.5-4B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-6-27b.md)
- [Qwen3.5-4B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-5-4b-vs-wevisone-vl-4b.md)
- [Qwen3.5-9B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-6-27b.md)
- [Qwen3.5-9B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-4b.md)
- [Qwen3.6-27B vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-6-35b-a3b.md)
- [Qwen3.6-27B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-8-27b.md)
- [Qwen3.6-27B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-8-flash-next.md)
- [Qwen3.6-27B vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-coder-30b-a3b-instruct.md)
- [Qwen3.6-27B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-coder-next.md)
- [Qwen3.6-27B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-omni-30b-a3b-instruct.md)
- [Qwen3.6-27B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-2b-instruct.md)
- [Qwen3.6-27B vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-30b-a3b-instruct.md)
- [Qwen3.6-27B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-32b-instruct.md)
- [Qwen3.6-27B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-4b-instruct.md)
- [Qwen3.6-27B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-8b-instruct.md)
- [Qwen3.6-27B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwopus3-8-27b-flash-v2.md)
- [Qwen3.6-27B vs Recursive Flint](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-recursive-flint.md)
- [Qwen3.6-27B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-ripple-65b-preview.md)
- [Qwen3.6-27B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-sensenova-loop-9b-preview.md)
- [Qwen3.6-27B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-smollm3-3b.md)
- [Qwen3.6-27B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-spark-x2-6-4b.md)
- [Qwen3.6-27B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-step-3-7-flash.md)
- [Qwen3.6-27B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-27b.md)
- [Qwen3.6-27B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-flash-next.md)
- [Qwen3.6-27B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-thinkingcap-qwen3-8-27b.md)
- [Qwen3.6-27B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-unlimited-ocr-2.md)
- [Qwen3.6-27B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-wevisone-vl-2b.md)
- [Qwen3.6-27B vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-zaya1-8b.md)
- [Qwen3.6-35B-A3B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-wevisone-vl-4b.md)
- [Qwen3.8-27B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-wevisone-vl-4b.md)
- [Qwen3.8-Flash-Next vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-wevisone-vl-4b.md)
- [Qwen3-Coder-30B-A3B-Instruct vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-wevisone-vl-4b.md)
- [Qwen3-Coder-Next vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-coder-next-vs-wevisone-vl-4b.md)
- [Qwen3-Omni-30B-A3B-Instruct vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-wevisone-vl-4b.md)
- [Qwen3-VL-2B-Instruct vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-wevisone-vl-4b.md)
- [Qwen3-VL-30B-A3B-Instruct vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-wevisone-vl-4b.md)
- [Qwen3-VL-32B-Instruct vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-4b.md)
- [Qwen3-VL-4B-Instruct vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-4b.md)
- [Qwen3-VL-8B-Instruct vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-4b.md)
- [Qwopus3.8-27B-Flash-V2 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-4b.md)
- [Recursive Flint vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-4b.md)
- [Ripple-65B Preview vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-4b.md)
- [SenseNova-Loop-9B-Preview vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b.md)
- [SmolLM3-3B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-4b.md)
- [Spark-X2.6-4B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-4b.md)
- [Step 3.7 Flash vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-4b.md)
- [Swift 1.5 Qwen3.8-27B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-wevisone-vl-4b.md)
- [Swift 1.5 Qwen3.8-Flash-Next vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-4b.md)
- [ThinkingCap (Qwen3.8 27B) vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-4b.md)
- [WeVisOne-VL-2B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-wevisone-vl-4b.md)
- [WeVisOne-VL-4B vs ZAYA1-8B](https://www.anchorterminal.com/compare/wevisone-vl-4b-vs-zaya1-8b.md)
- [Baidu Unlimited-OCR-2 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-4b.md)

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