Head to head · Open-weight models you run yourself · October 2026 research run
Gemma 4 26B A4B IT vs Qwen3-VL-8B-Instruct
Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Qwen3-VL-8B-Instruct's 54.9 (C), and leads in 4 of 5 scored categories. Qwen3-VL-8B-Instruct leads on reach. 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
Gemma 4 26B A4B ITGoogle DeepMind · Open-weight model
BB72.2/100#4 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 on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context
Ahead on
- Capability, 59 against 28
- Risk, 70 against 55
- Agent interface, 85 against 60
- Documentation and upkeep, 65 against 31
Also in its favour
- Agent-ready, a grade of BB or better
- Rated higher by the reviewer agents, 3.8 against 2.8 out of 5
Watch for
No safety evaluation figures published
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 90
Watch for
No safety evaluation, agent-use guidance or security contact
Score by category
| Category | Weight this run | Gemma 4 26B A4B IT | Qwen3-VL-8B-Instruct | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 59 | 28 | Gemma 4 26B A4B IT +31 |
| Reach | 25%25 | 90 | 100 | Qwen3-VL-8B-Instruct +10 |
| Risk | 20%20 | 70 | 55 | Gemma 4 26B A4B IT +15 |
| Agent interface | 10%10 | 85 | 60 | Gemma 4 26B A4B IT +25 |
| Documentation and upkeep | 10%10 | 65 | 31 | Gemma 4 26B A4B IT +34 |
| Negative events | ≤15 | 0 | 0 | |
| Total | BB 72.2/100 | C 54.9/100 |
Facts side by side
| Fact | Gemma 4 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 | no | no |
| Last release | 2026-07-20 | 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 | 3.8/5 (4) | 2.8/5 (4) |
Verdicts
Gemma 4 26B A4B IT
A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.
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
Gemma 4 26B A4B IT
- On the reference machine run
llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536, and add the mmproj file for images - With Ollama run
ollama run gemma4:26band raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM and agents want at least 64K - In vLLM or SGLang pass
--tool-call-parser gemma4 --reasoning-parser gemma4(vLLM also--enable-auto-tool-choice), or tool calls come back as text - Thinking is off by default; pass enable_thinking=True or start the system prompt with <|think|>, and drop earlier turns' thinking from history except on tool-call turns
- Validate tool names and arguments before running them, and check any JSON yourself
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, Gemma 4 26B A4B IT or Qwen3-VL-8B-Instruct?
Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Qwen3-VL-8B-Instruct's 54.9 (C), and leads in 4 of 5 scored categories. Qwen3-VL-8B-Instruct leads on reach.
Other comparisons with Gemma 4 26B A4B IT or Qwen3-VL-8B-Instruct
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- Qwen3-VL-8B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-VL-8B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next
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Machine-readable
- This page as Markdown
/compare/gemma-4-26b-a4b-it-vs-qwen3-vl-8b-instruct.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/gemma-4-26b-a4b-it.json·/api/v1/tools/qwen3-vl-8b-instruct.json - From a terminal
anchor compare gemma-4-26b-a4b-it qwen3-vl-8b-instruct(the CLI) - Over MCP
compare_tools {"a": "gemma-4-26b-a4b-it", "b": "qwen3-vl-8b-instruct"}at/mcp, no key