# Inkling-Small vs WeVisOne-VL-2B

> Inkling-Small scores 67 (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/inkling-small-vs-wevisone-vl-2b
- Markdown: https://www.anchorterminal.com/compare/inkling-small-vs-wevisone-vl-2b.md (~9,500 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

Inkling-Small scores 67 (B) on agent readiness against WeVisOne-VL-2B's 61 (C), and leads in 3 of 4 scored categories. WeVisOne-VL-2B leads on reach. Both do open-weight models you run yourself.

- Inkling-Small: grade B, 67/100, rank #13 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/inkling-small.md · JSON https://www.anchorterminal.com/api/v1/tools/inkling-small.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

### Inkling-Small (B)

Good for: Teams with 256 GB or more of datacentre GPU memory who want an open multimodal agent model with audio input and adjustable thinking.

Ahead on:
- Risk, 70 against 55
- Agent interface, 85 against 70
- Documentation and upkeep, 87 against 48

Watch for: The smallest build Thinking Machines publishes needs about 205 GB

### 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:
- Reach, 70 against 55

Watch for: The pixel decoder ships as a pickle file


## Why the edge

### Documentation and upkeep: Inkling-Small +39

- Inkling-Small (87): Model card 17 of 20, everything but a dated knowledge cut-off, counting Thinking Machines' own model card page. Training data 15, an EU training-content summary with sources, mix, a July 2026 collection cut-off and filtering. Last change 20, the files last changed on 31 July 2026, but Thinking Machines' Inkling renderer in tinker-cookbook changed in its 30 October 2026 release. Responsiveness 20, Thinking Machines answered 8 of the 9 questions in its discussions. Version policy 5, a first release. File health 10, the files agree and no broken-file report waits. 17+15+20+20+5+10 = 87.
- 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: WeVisOne-VL-2B +15

- Inkling-Small (55): Memory 5 of 30, the smallest 4-bit build is Thinking Machines' NVFP4 at 170.8 GB, about 205 GB with a fifth added, so it needs 256 GB. Runtimes 15, released Transformers, vLLM and SGLang run it, llama.cpp, Ollama and LM Studio don't. Builds 10, one quantised build (NVFP4). Terms 10, Apache 2.0 with a binding acceptable use policy on top, read as a custom licence. Download 10, no gate. Hosted 5, Thinking Machines' own Tinker serves it as beta serverless inference, and DeepInfra is live on Hugging Face.
- 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.

### Risk: Inkling-Small +15

- Inkling-Small (70): File safety 25, safetensors only and no repository code to run. Provenance 15, the files are in the organisation Thinking Machines links to, with a SHA-256 on every LFS file. Safety evaluation 10, Thinking Machines' own StrongREJECT and FORTRESS results and described external red-teaming, but nothing an outside party published or a harness to rerun. Agent guidance 10, the card says what its refusals miss and which safeguards to add, with nothing on prompt injection. Licence risk 5, 10 off for a use policy that reaches beyond the law and that can change at any time. Security programme 5, a security.txt and a disclosure policy, with public advisories unchecked and scored as absent.
- WeVisOne-VL-2B (55): File safety 15, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it. Provenance 15, the files sit in the tencent organisation, which Tencent's GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card or in the README. Agent guidance 5, General cautions: the card says the release is for research, isn't for autonomous high-impact decisions or identifying individuals, and lists where masks go wrong. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide, as the Hy3 listing records (5). 15 + 15 + 0 + 5 + 15 + 5 = 55.

### Agent interface: Inkling-Small +15

- Inkling-Small (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, Thinking Machines' tml-renderers guide shows how tools are declared and how calls come back, and its package ships the parser. Trained tool calling 20, several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking effort is set per request from none to the maximum, and the reasoning comes back in reasoning_content. Context 10, up to 1M tokens.
- 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

| Category | Weight | Inkling-Small | WeVisOne-VL-2B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 55 | 70 | WeVisOne-VL-2B +15 |
| Risk | 20% (20 this run) | 70 | 55 | Inkling-Small +15 |
| Agent interface | 10% (10 this run) | 85 | 70 | Inkling-Small +15 |
| Documentation and upkeep | 10% (10 this run) | 87 | 48 | Inkling-Small +39 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **67 · B** | **61 · C** | |

## Facts side by side

| Fact | Inkling-Small | WeVisOne-VL-2B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Thinking Machines Lab | 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 | 2026-07-31 | 2026-10-28 |
| Terms last updated | no document linked | not read yet |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 3.5/5 (4) | none |

## How the scores have moved

### Inkling-Small

- +0.5 on 1 November 2026: product

### WeVisOne-VL-2B

No score change since it was listed.

## Verdicts

**Inkling-Small.** A mixture-of-experts generalist with images and audio in, several tool calls a turn and per-request thinking effort. It needs about 205 GB for Thinking Machines' NVFP4 build on recent NVIDIA or AMD datacentre GPUs, and Tinker or DeepInfra host it. The main risks are a use policy that binds on download and can change, and no outside safety results.

**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

### Inkling-Small

1. Start the NVFP4 build with SGLang's recipe, `sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-Small-NVFP4 --tp 8 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling`, on Blackwell for W4A4
2. Use the inkling tool and reasoning parsers in vLLM or SGLang, or Thinking Machines' tml-renderers package to render prompts and parse calls yourself
3. Pass `reasoning_effort` as none, minimal, low, medium, high (the default) or max, or a number from 0 to 0.99. The repository's template has no xhigh preset and raises an error for it, though Thinking Machines' docs and SGLang's cookbook list xhigh
4. Effort none conditions the model toward no reasoning but doesn't forbid it, and higher effort needs a larger max_tokens
5. Tool call arguments must reach the template as an object, not a JSON string, or it raises an error
6. Images should be 40 to 4096 px a side and audio 16 kHz WAV under two minutes, per the card
7. Use guided decoding or validate JSON yourself, since structured output isn't documented
8. Read the Model Acceptable Use Policy before deploying, it binds on download and bars some uses that are legal

### 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, Inkling-Small or WeVisOne-VL-2B?

Inkling-Small scores 67 (B) on agent readiness against WeVisOne-VL-2B's 61 (C), and leads in 3 of 4 scored categories. WeVisOne-VL-2B leads on reach.


## For agents

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

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- [Inkling-Small vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/inkling-small-vs-nanbeige-4-2-3b.md)
- [Inkling-Small vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/inkling-small-vs-nanbeige-4-3-33b.md)
- [Inkling-Small vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-5-bolt-e4b-4gb.md)
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- [Inkling-Small vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-0-8b.md)
- [Inkling-Small vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-122b-a10b.md)
- [Inkling-Small vs Qwen3.5-2B](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-2b.md)
- [Inkling-Small vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-397b-a17b.md)
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- [Inkling-Small vs Qwen3.5-9B](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-9b.md)
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- [Inkling-Small vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-6-35b-a3b.md)
- [Inkling-Small vs Qwen3.8-27B](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-8-27b.md)
- [Inkling-Small vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-8-flash-next.md)
- [Inkling-Small vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-coder-30b-a3b-instruct.md)
- [Inkling-Small vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-coder-next.md)
- [Inkling-Small vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-omni-30b-a3b-instruct.md)
- [Inkling-Small vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-2b-instruct.md)
- [Inkling-Small vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-30b-a3b-instruct.md)
- [Inkling-Small vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-32b-instruct.md)
- [Inkling-Small vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-4b-instruct.md)
- [Inkling-Small vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-8b-instruct.md)
- [Inkling-Small vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/inkling-small-vs-qwopus3-8-27b-flash-v2.md)
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- [Inkling-Small vs SmolLM3-3B](https://www.anchorterminal.com/compare/inkling-small-vs-smollm3-3b.md)
- [Inkling-Small vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/inkling-small-vs-spark-x2-6-4b.md)
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- [Inkling-Small vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/inkling-small-vs-swift-1-5-qwen3-8-27b.md)
- [Inkling-Small vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/inkling-small-vs-swift-1-5-qwen3-8-flash-next.md)
- [Inkling-Small vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/inkling-small-vs-thinkingcap-qwen3-8-27b.md)
- [Inkling-Small vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/inkling-small-vs-unlimited-ocr-2.md)
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- [Inkling-Small vs ZAYA1-8B](https://www.anchorterminal.com/compare/inkling-small-vs-zaya1-8b.md)
- [Inkling vs Inkling-Small](https://www.anchorterminal.com/compare/inkling-vs-inkling-small.md)
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- [LFM3-VL-2B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-2b.md)
- [Ling-3.0-tiny vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-wevisone-vl-2b.md)
- [Ling-3.1-flash-1019 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-wevisone-vl-2b.md)
- [LongCat-2.5 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-2b.md)
- [MiMo-V2.6 Distill Qwen 9B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-wevisone-vl-2b.md)
- [MiMo-V2.6-Flash-RL vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-2b.md)
- [MiniCPM-o 4.5 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-wevisone-vl-2b.md)
- [MiniCPM-V 4.7 1B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-2b.md)
- [MiniCPM-V 4.7 35B-A3B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-2b.md)
- [MiniCPM5-2B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minicpm5-2b-vs-wevisone-vl-2b.md)
- [MiniMax-M2.5 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-wevisone-vl-2b.md)
- [MiniMax-M2.7 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-2b.md)
- [MiniMax-M3.1-Flash vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-wevisone-vl-2b.md)
- [Mistral Large 4 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/mistral-large-4-vs-wevisone-vl-2b.md)
- [Muse Glimmer 30B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-wevisone-vl-2b.md)
- [Nanbeige4.2-3B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-2b.md)
- [Nanbeige4.3-33B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-wevisone-vl-2b.md)
- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-wevisone-vl-2b.md)
- [Nemotron 3.5 Lightning 30B A3B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-wevisone-vl-2b.md)
- [Nemotron 3 Nano 30B A3B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-wevisone-vl-2b.md)
- [Nemotron 3 Nano 4B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-2b.md)
- [Nemotron 3 Nano Omni 30B A3B Reasoning vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-wevisone-vl-2b.md)
- [Nemotron 3 Super 120B A12B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-2b.md)
- [Nemotron 3 Ultra 550B A55B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-wevisone-vl-2b.md)
- [North Mini Code 1.0 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-2b.md)
- [occamy-1.0 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-2b.md)
- [Olmo 3 7B Think vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-wevisone-vl-2b.md)
- [Ornith-1.5-122B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-2b.md)
- [Ornith-1.5-35B-A3B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-wevisone-vl-2b.md)
- [Ornith-1.5-397B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-wevisone-vl-2b.md)
- [Ornith-1.5-9B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-wevisone-vl-2b.md)
- [OTel-2.0-LLM-31B-IT vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-2b.md)
- [Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-2b.md)
- [Qwen3-30B-A3B-Instruct-2507 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-wevisone-vl-2b.md)
- [Qwen3-4B-Instruct-2507 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-wevisone-vl-2b.md)
- [Qwen3.5-0.8B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-wevisone-vl-2b.md)
- [Qwen3.5-122B-A10B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-wevisone-vl-2b.md)
- [Qwen3.5-2B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-wevisone-vl-2b.md)
- [Qwen3.5-397B-A17B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-wevisone-vl-2b.md)
- [Qwen3.5-4B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-5-4b-vs-wevisone-vl-2b.md)
- [Qwen3.5-9B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-2b.md)
- [Qwen3.6-27B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-wevisone-vl-2b.md)
- [Qwen3.6-35B-A3B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-wevisone-vl-2b.md)
- [Qwen3.8-27B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-wevisone-vl-2b.md)
- [Qwen3.8-Flash-Next vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-wevisone-vl-2b.md)
- [Qwen3-Coder-30B-A3B-Instruct vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-wevisone-vl-2b.md)
- [Qwen3-Coder-Next vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-coder-next-vs-wevisone-vl-2b.md)
- [Qwen3-Omni-30B-A3B-Instruct vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-wevisone-vl-2b.md)
- [Qwen3-VL-2B-Instruct vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-wevisone-vl-2b.md)
- [Qwen3-VL-30B-A3B-Instruct vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-wevisone-vl-2b.md)
- [Qwen3-VL-32B-Instruct vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-2b.md)
- [Qwen3-VL-4B-Instruct vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-2b.md)
- [Qwen3-VL-8B-Instruct vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-2b.md)
- [Qwopus3.8-27B-Flash-V2 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-2b.md)
- [Recursive Flint vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-2b.md)
- [Ripple-65B Preview vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-2b.md)
- [SenseNova-Loop-9B-Preview vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b.md)
- [SmolLM3-3B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-2b.md)
- [Spark-X2.6-4B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-2b.md)
- [Step 3.7 Flash vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-2b.md)
- [Swift 1.5 Qwen3.8-27B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-wevisone-vl-2b.md)
- [Swift 1.5 Qwen3.8-Flash-Next vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-2b.md)
- [ThinkingCap (Qwen3.8 27B) vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-2b.md)
- [WeVisOne-VL-2B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-wevisone-vl-4b.md)
- [WeVisOne-VL-2B vs ZAYA1-8B](https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-zaya1-8b.md)
- [Baidu Unlimited-OCR-2 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-2b.md)

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