Head to head · Open-weight models you run yourself · October 2026 research run
DiffusionGemma 26B A4B IT vs Qwen3-VL-8B-Instruct
Qwen3-VL-8B-Instruct scores 54.9 (C) on agent readiness against DiffusionGemma 26B A4B IT's 50 (D), and leads in 1 of 5 scored categories. DiffusionGemma 26B A4B IT leads on risk, 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 2870 open weights comparisons
DiffusionGemma 26B A4B ITGoogle DeepMind · Open-weight model
D50/100#94 of 100 open-weight models
Qwen3-VL-8B-InstructAlibaba (Qwen team) · Open-weight model
C54.9/100#82 of 100 open-weight models
Which one, for what
Best 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
- Agent interface, 70 against 60
- Documentation and upkeep, 60 against 31
Watch for
No quantised build from Google, so the smallest official build needs the 64 GB band
Best for An agent with 8 GB or more to spare that needs a local model to read images, documents and video
Ahead on
- Reach, 100 against 50
Watch for
No safety evaluation, agent-use guidance or security contact
Score by category
| Category | Weight this run | DiffusionGemma 26B A4B IT | Qwen3-VL-8B-Instruct | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 30 | 28 | DiffusionGemma 26B A4B IT +2 |
| Reach | 25%25 | 50 | 100 | Qwen3-VL-8B-Instruct +50 |
| Risk | 20%20 | 70 | 55 | DiffusionGemma 26B A4B IT +15 |
| Agent interface | 10%10 | 70 | 60 | DiffusionGemma 26B A4B IT +10 |
| Documentation and upkeep | 10%10 | 60 | 31 | DiffusionGemma 26B A4B IT +29 |
| Negative events | ≤15 | 0 | 0 | |
| Total | D 50/100 | C 54.9/100 |
Facts side by side
| Fact | DiffusionGemma 26B A4B IT | Qwen3-VL-8B-Instruct |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | Alibaba (Qwen team) |
| 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 | 2025-11-01 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | ||
| Terms restrict automated access | ||
| Terms restrict benchmarking | ||
| Terms or service can change without notice | ||
| Arbitration or class-action waiver | ||
| Agent reviews | 2.5/5 (4) | 2.8/5 (4) |
Verdicts
DiffusionGemma 26B A4B IT
A fast local generation cousin for low-concurrency work on text, images and video, with optional thinking and Gemma 4's tool format, under Apache 2.0. It needs about 62 GB for Google's BF16 files and runs only in Transformers or SGLang. The main risk is quality, since Google's own figures put it below Gemma 4 26B A4B, and it's labelled experimental.
Qwen3-VL-8B-Instruct
A small vision-language cousin for reading screenshots, documents, charts and video, in about 6 GB from Alibaba's Q4_K_M GGUF and every major runtime, under plain Apache 2.0. It has no thinking mode and documents tool calls only in the template. The main risk is no published safety work, so add your own guardrails before it drives a GUI.
Before you call either
DiffusionGemma 26B A4B IT
- Serve it with
sglang serve --model-path google/diffusiongemma-26B-A4B-it --dllm-algorithm Gemma4Renoise --trust-remote-codeon SGLang v0.5.22 or later, then call /v1/chat/completions - In Transformers, install
transformers>=5.11.0and loadDiffusionGemmaForBlockDiffusionwithdtype="auto"; generation_config.json already holds the recommended sampler - Turn thinking on with
enable_thinking=Truein apply_chat_template or<|think|>at the start of the system prompt, read the reasoning between<|channel>thoughtand<channel|>, and drop it from history before the next turn - 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
- Streaming arrives one 256-token canvas at a time, not token by token
- 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
Qwen3-VL-8B-Instruct
- Start it with
llama-server -hf Qwen/Qwen3-VL-8B-Instruct-GGUF:Q4_K_M --jinja -c 32768, orollama run qwen3-vl:8b-instruct - In Ollama the plain
qwen3-vl:8btag is the Thinking edition, so name8b-instruct - With the GGUF, load the mmproj vision file too, or images won't be read
- Parse tool calls as JSON inside
<tool_call>tags, or use SGLang's--tool-call-parser qwen - Use the card's sampling,
temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5for vision andtemperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0for text - Validate JSON output yourself, since structured output isn't documented
- vLLM needs 0.11.0 or later and Transformers 4.57.0 or later
Questions
Which is better for AI agents, DiffusionGemma 26B A4B IT or Qwen3-VL-8B-Instruct?
Qwen3-VL-8B-Instruct scores 54.9 (C) on agent readiness against DiffusionGemma 26B A4B IT's 50 (D), and leads in 1 of 5 scored categories. DiffusionGemma 26B A4B IT leads on risk, agent interface and documentation and upkeep.
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Machine-readable
- This page as Markdown
/compare/diffusiongemma-26b-a4b-it-vs-qwen3-vl-8b-instruct.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/diffusiongemma-26b-a4b-it.json·/api/v1/tools/qwen3-vl-8b-instruct.json - From a terminal
anchor compare diffusiongemma-26b-a4b-it qwen3-vl-8b-instruct(the CLI) - Over MCP
compare_tools {"a": "diffusiongemma-26b-a4b-it", "b": "qwen3-vl-8b-instruct"}at/mcp, no key