# DiffusionGemma 26B A4B IT vs WeVisOne-VL-4B > WeVisOne-VL-4B scores 61 (C) to DiffusionGemma 26B A4B IT's 51.3 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-wevisone-vl-4b - Markdown: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-wevisone-vl-4b.md (~9,900 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 .grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.. and .vs.. 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 WeVisOne-VL-4B scores 61 (C) on agent readiness against DiffusionGemma 26B A4B IT's 51.3 (D), and leads in 1 of 4 scored categories. DiffusionGemma 26B A4B IT leads on risk and documentation and upkeep. Both do open-weight models you run yourself. - DiffusionGemma 26B A4B IT: grade D, 51.3/100, rank #143 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/diffusiongemma-26b-a4b-it.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 ### DiffusionGemma 26B A4B IT (D) Good for: Agents with about 64 GB of GPU memory that want fast single-user generation from text and images and can accept lower quality than Gemma 4 Ahead on: - Risk, 70 against 55 - Documentation and upkeep, 60 against 48 Watch for: No quantised build from Google, so the smallest official build needs the 64 GB band ### 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: - Reach, 70 against 55 Watch for: The pixel decoder ships as a pickle file ## Why the edge ### Reach: WeVisOne-VL-4B +15 - DiffusionGemma 26B A4B IT (55): Memory earns 15 because the only Google build is 51.7 GB of BF16, about 62 GB with the fifth added, which needs the 64 GB band. Runtimes get 15 for Transformers from 5.11.0, SGLang from v0.5.22 and vLLM, whose releases from v0.24.0 (tag commit 28 June 2026) to v0.33.0 register DiffusionGemmaForBlockDiffusion; llama.cpp (b12114, and Ollama through its pin b11963) and LM Studio don't run it. Builds score 0 because Google publishes no quantised build, though others have published FP8, NVFP4, GGUF and 4-bit copies. Apache 2.0 with nothing added gives terms the full 15 and the ungated repository gives download the full 10. Hosted too scores 0, since no inference provider is mapped and the Gemini API doesn't serve it. - 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. ### Risk: DiffusionGemma 26B A4B IT +15 - DiffusionGemma 26B A4B IT (70): File safety gets the full 25, safetensors only and loaded by Transformers' own code and a Jinja template. Provenance gets 15 for files in Google's organisation with a hash for every file. Licence risk keeps 15 under plain Apache 2.0. Agent guidance gets 10 for the card's list of harm categories and its advice to add content-safety safeguards. Safety evaluation scores 0 because the card states improvements without figures. The security programme gets 5 for Google's security.txt, with no public advisories for Gemma. - WeVisOne-VL-4B (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. ### Documentation and upkeep: DiffusionGemma 26B A4B IT +12 - DiffusionGemma 26B A4B IT (60): The card covers all seven points (20), and the training data is a general description with a January 2025 cut-off (10). The files last changed on 15 July 2026, 112 days before this check (10). Google organisation members answered 10 of the 20 discussions (10). It's a first release (5). File health gets 5 because the diffusers scheduler config disagrees with the card's sampling settings, though the files on the documented path agree. - 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 | DiffusionGemma 26B A4B IT | WeVisOne-VL-4B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 55 | 70 | WeVisOne-VL-4B +15 | | Risk | 20% (20 this run) | 70 | 55 | DiffusionGemma 26B A4B IT +15 | | Agent interface | 10% (10 this run) | 70 | 70 | even | | Documentation and upkeep | 10% (10 this run) | 60 | 48 | DiffusionGemma 26B A4B IT +12 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **51.3 · D** | **61 · C** | | ## Facts side by side | Fact | DiffusionGemma 26B A4B IT | WeVisOne-VL-4B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Google DeepMind | 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 | yes | no | | Last release | 2026-07-15 | 2026-10-27 | | Terms last updated | no document linked | not read yet | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | 2.5/5 (4) | none | ## How the scores have moved ### DiffusionGemma 26B A4B IT - +1.3 on 4 November 2026: correction ### WeVisOne-VL-4B No score change since it was listed. ## Verdicts **DiffusionGemma 26B A4B IT.** A text diffusion model for fast low-concurrency local generation from text, images and video, with optional thinking and Gemma 4's tool format, under Apache 2.0. Google's BF16 files need about 62 GB, and it runs in Transformers, SGLang and vLLM. Google's own figures put it below Gemma 4 26B A4B, and Google labels it experimental. **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 ### DiffusionGemma 26B A4B IT 1. Serve it with `sglang serve --model-path google/diffusiongemma-26B-A4B-it --dllm-algorithm Gemma4Renoise --trust-remote-code` on SGLang v0.5.22 or later, then call /v1/chat/completions 2. In Transformers, install `transformers>=5.11.0` and load `DiffusionGemmaForBlockDiffusion` with `dtype="auto"`; generation_config.json already holds the recommended sampler 3. Turn thinking on with `enable_thinking=True` in apply_chat_template or `<|think|>` at the start of the system prompt, read the reasoning between `<|channel>thought` and ``, and drop it from history before the next turn 4. Don't rely on temperature, top_p, logit_bias or JSON Schema in SGLang, which ignores or rejects them for this model; validate JSON yourself 5. Streaming arrives one 256-token canvas at a time, not token by token 6. Put images before text in the prompt, and pick an image token budget of 70 to 1,120 by how much detail the task needs ### 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, DiffusionGemma 26B A4B IT or WeVisOne-VL-4B? WeVisOne-VL-4B scores 61 (C) on agent readiness against DiffusionGemma 26B A4B IT's 51.3 (D), and leads in 1 of 4 scored categories. DiffusionGemma 26B A4B IT leads on risk and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-wevisone-vl-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-wevisone-vl-4b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "diffusiongemma-26b-a4b-it", "b": "wevisone-vl-4b"}`. 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