Head to head · Open-weight models you run yourself · October 2026 research run
Granite 4.2 3B vs WeVisOne-VL-4B
WeVisOne-VL-4B scores 61 (C) on agent readiness against Granite 4.2 3B's 59.8 (C), and leads in 0 of 4 scored categories. Granite 4.2 3B leads on reach, 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
Which one, for what
Best for Agents on small hardware that want a permissive reasoning model for tool calls and coding, with thinking they can switch off
Ahead on
- Reach, 95 against 70
- Agent interface, 85 against 70
- Documentation and upkeep, 92 against 48
Watch for
No safety results published for 4.2, and only general safety cautions
Best for Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence
No category where it leads by five points or more, and no fact that sets it apart.
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 Granite 4.2 3B +44
Granite 4.2 3B 92
The card covers everything but a knowledge cut-off (17). IBM's disclosure lists the datasets with sources and sizes (20). The index file changed on 20 October 2026, 15 days before this check, and nothing in IBM's 4.2 repositories or the GitHub repository changed since (20). The two questions asked on IBM's 3B repositories have an IBM reply, and no new thread was opened after 21 October (20). Granite 4.1 3B stays in its own repository with no note (5), and the files agree with each other (10).
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 Granite 4.2 3B +25
Granite 4.2 3B 95
Memory earns the full 30, since IBM's 4-bit MLX build is 2.07 GB and Ollama's Q4_K_M build 2.2 GB, about 2.5 to 2.6 GB with the fifth added. Five named runtimes run the granite architecture in released versions or their own libraries, so runtimes take the cap of 25, though LM Studio has no 4.2 entry. IBM's FP8, FP4, GGUF and MLX builds earn 15. Apache 2.0 with nothing added gives terms 15 and the ungated repositories download 10. Hosted too is 0, since only DeepInfra serves it and watsonx.ai stops at 4.1.
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.
Agent interface Granite 4.2 3B +15
Granite 4.2 3B 85
The template ships in the repository (20). The card shows how tools are declared and how calls come back, and names qwen3_coder for vLLM and SGLang (25). The template renders several calls in a turn (20). JSON output isn't documented for 4.2 (0). Thinking turns off or goes to low effort per request, with the reasoning returned apart in SGLang (10), and 131,072 tokens are native (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
| Category | Weight this run | Granite 4.2 3B | WeVisOne-VL-4B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 95 | 70 | Granite 4.2 3B +25 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 85 | 70 | Granite 4.2 3B +15 |
| Documentation and upkeep | 10%10 | 92 | 48 | Granite 4.2 3B +44 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 59.8/100 | C 61/100 |
Facts side by side
| Fact | Granite 4.2 3B | WeVisOne-VL-4B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | IBM | 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 | 2026-10-20 | 2026-10-27 |
| Terms last updated | no document linked | not read yet |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 3.8/5 (4) | none |
Verdicts
Granite 4.2 3B
A small reasoning model for tool calls, coding and multilingual chat, published by IBM under plain Apache 2.0 with signed files and a dataset disclosure, and run by llama.cpp, Ollama, vLLM, SGLang and Transformers from a 2.2 GB Ollama build. Thinking turns off or shortens per request. It has no published safety results and no documented JSON mode.
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
Granite 4.2 3B
- Start it with
ollama run granite4.2:3b; set options.num_ctx in the request, since Ollama's default context is short and the model takes up to 131,072 - With vLLM 0.32.0 or later, serve
ibm-granite/granite-4.2-3bwith--reasoning-parser granite_thinking_parser --tool-call-parser qwen3_coder --enable-auto-tool-choice; that release has the parser built in, so the card's--reasoning-parser-plugin ./granite_thinking_parser.pyisn't needed - With SGLang 0.5.18 or later,
--reasoning-parser auto --tool-call-parser autoresolve to IBM's recommended parsers, and the reasoning comes back in reasoning_content - Use temperature 1.0 and top_p 0.95 for every mode, as IBM requires
- Pass
enable_thinkingfalse for direct answers orlow_efforttrue for short reasoning, through chat_template_kwargs on an OpenAI-compatible server - Earlier turns' thinking is stripped by default (
truncate_history_thinking), so pass it false to keep reasoning in multi-turn history - For JSON, use the runtime's guided decoding, since IBM documents no JSON mode for 4.2
- Verify the files with
python -m model_signing verify sigstoreand IBM's identity, per IBM's signature guide
WeVisOne-VL-4B
- Use Tencent's package:
WeVisOneVL("tencent/WeVisOne-VL-4B", backend="vllm")thenmodel.predict(image, task=..., query=...) - Tasks are detection, referring, point, count_box, count_point, ocr, ocr_grounding, segmentation and matting
- A None answer means the target isn't in the image. Treat it as a result, not an error
- mask_decoder.pt is a pickle. Load it only from the tencent repository, and check its SHA-256 against the Hub's
- Send one image a turn. Video grids are rejected by the decoder
- The card says it isn't for identifying individuals or for high-impact decisions on its own
Questions
Which is better for AI agents, Granite 4.2 3B or WeVisOne-VL-4B?
WeVisOne-VL-4B scores 61 (C) on agent readiness against Granite 4.2 3B's 59.8 (C), and leads in 0 of 4 scored categories. Granite 4.2 3B leads on reach, agent interface and documentation and upkeep.
Other comparisons with Granite 4.2 3B or WeVisOne-VL-4B
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- Granite 4.2 3B vs ZAYA1-8B
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- Hy3 vs WeVisOne-VL-4B
- Inkling-Small vs WeVisOne-VL-4B
- Inkling vs WeVisOne-VL-4B
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- Kolibri-1 vs WeVisOne-VL-4B
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- Laguna M.1 vs WeVisOne-VL-4B
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- LBS-27B vs WeVisOne-VL-4B
- LFM2.5-2.6B vs WeVisOne-VL-4B
- LFM3-1.7B vs WeVisOne-VL-4B
- LFM3-Audio-2B vs WeVisOne-VL-4B
- LFM3-Omni-2B vs WeVisOne-VL-4B
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- Ling-3.1-flash-1019 vs WeVisOne-VL-4B
- LongCat-2.5 vs WeVisOne-VL-4B
- MiMo-V2.6 Distill Qwen 9B vs WeVisOne-VL-4B
- MiMo-V2.6-Flash-RL vs WeVisOne-VL-4B
- MiniCPM-o 4.5 vs WeVisOne-VL-4B
- MiniCPM-V 4.7 1B vs WeVisOne-VL-4B
- MiniCPM-V 4.7 35B-A3B vs WeVisOne-VL-4B
- MiniCPM5-2B vs WeVisOne-VL-4B
- MiniMax-M2.5 vs WeVisOne-VL-4B
- MiniMax-M2.7 vs WeVisOne-VL-4B
- MiniMax-M3.1-Flash vs WeVisOne-VL-4B
- Mistral Large 4 vs WeVisOne-VL-4B
- Muse Glimmer 30B vs WeVisOne-VL-4B
- Nanbeige4.2-3B vs WeVisOne-VL-4B
- Nanbeige4.3-33B vs WeVisOne-VL-4B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs WeVisOne-VL-4B
- Nemotron 3.5 Lightning 30B A3B vs WeVisOne-VL-4B
- Nemotron 3 Nano 30B A3B vs WeVisOne-VL-4B
- Nemotron 3 Nano 4B vs WeVisOne-VL-4B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs WeVisOne-VL-4B
- Nemotron 3 Super 120B A12B vs WeVisOne-VL-4B
- Nemotron 3 Ultra 550B A55B vs WeVisOne-VL-4B
- North Mini Code 1.0 vs WeVisOne-VL-4B
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- Olmo 3 7B Think vs WeVisOne-VL-4B
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- Ornith-1.5-35B-A3B vs WeVisOne-VL-4B
- Ornith-1.5-397B vs WeVisOne-VL-4B
- Ornith-1.5-9B vs WeVisOne-VL-4B
- OTel-2.0-LLM-31B-IT vs WeVisOne-VL-4B
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- Qwen3-30B-A3B-Instruct-2507 vs WeVisOne-VL-4B
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- Qwen3.5-397B-A17B vs WeVisOne-VL-4B
- Qwen3.5-4B vs WeVisOne-VL-4B
- Qwen3.5-9B vs WeVisOne-VL-4B
- Qwen3.6-27B vs WeVisOne-VL-4B
- Qwen3.6-35B-A3B vs WeVisOne-VL-4B
- Qwen3.8-27B vs WeVisOne-VL-4B
- Qwen3.8-Flash-Next vs WeVisOne-VL-4B
- Qwen3-Coder-30B-A3B-Instruct vs WeVisOne-VL-4B
- Qwen3-Coder-Next vs WeVisOne-VL-4B
- Qwen3-Omni-30B-A3B-Instruct vs WeVisOne-VL-4B
- Qwen3-VL-2B-Instruct vs WeVisOne-VL-4B
- Qwen3-VL-30B-A3B-Instruct vs WeVisOne-VL-4B
- Qwen3-VL-32B-Instruct vs WeVisOne-VL-4B
- Qwen3-VL-4B-Instruct vs WeVisOne-VL-4B
- Qwen3-VL-8B-Instruct vs WeVisOne-VL-4B
- Qwopus3.8-27B-Flash-V2 vs WeVisOne-VL-4B
- Recursive Flint vs WeVisOne-VL-4B
- Ripple-65B Preview vs WeVisOne-VL-4B
- SenseNova-Loop-9B-Preview vs WeVisOne-VL-4B
- SmolLM3-3B vs WeVisOne-VL-4B
- Spark-X2.6-4B vs WeVisOne-VL-4B
- Step 3.7 Flash vs WeVisOne-VL-4B
- Swift 1.5 Qwen3.8-27B vs WeVisOne-VL-4B
- Swift 1.5 Qwen3.8-Flash-Next vs WeVisOne-VL-4B
- ThinkingCap (Qwen3.8 27B) vs WeVisOne-VL-4B
- WeVisOne-VL-2B vs WeVisOne-VL-4B
- WeVisOne-VL-4B vs ZAYA1-8B
- Baidu Unlimited-OCR-2 vs WeVisOne-VL-4B
Machine-readable
- This page as Markdown
/compare/granite-4-2-3b-vs-wevisone-vl-4b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/granite-4-2-3b.json·/api/v1/tools/wevisone-vl-4b.json - From a terminal
anchor compare granite-4-2-3b wevisone-vl-4b(the CLI) - Over MCP
compare_tools {"a": "granite-4-2-3b", "b": "wevisone-vl-4b"}at/mcp, no key