Head to head · Open-weight models you run yourself · October 2026 research run
Qwen3-VL-30B-A3B-Instruct vs Qwen3-VL-4B-Instruct
Qwen3-VL-30B-A3B-Instruct scores 58 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 2 of 5 scored categories. 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
Qwen3-VL-30B-A3B-InstructAlibaba (Qwen team) · Open-weight model
C58/100#68 of 100 open-weight models
Qwen3-VL-4B-InstructAlibaba (Qwen team) · Open-weight model
D52.6/100#84 of 100 open-weight models
Which one, for what
Best for An agent with 24 GB or more to spare that wants a faster, stronger local reader of images, documents and video than the 8B model
Ahead on
- Capability, 39 against 25
- Documentation and upkeep, 36 against 31
Watch for
No safety evaluation, agent-use guidance or security contact
Best for An agent with 4 GB or more to spare that needs a local model to read images, documents and video
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
No safety evaluation, agent-use guidance or security contact
Score by category
| Category | Weight this run | Qwen3-VL-30B-A3B-Instruct | Qwen3-VL-4B-Instruct | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 39 | 25 | Qwen3-VL-30B-A3B-Instruct +14 |
| Reach | 25%25 | 95 | 95 | even |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 60 | 60 | even |
| Documentation and upkeep | 10%10 | 36 | 31 | Qwen3-VL-30B-A3B-Instruct +5 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 58/100 | D 52.6/100 |
Facts side by side
| Fact | Qwen3-VL-30B-A3B-Instruct | Qwen3-VL-4B-Instruct |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Alibaba (Qwen team) | 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 | 2025-11-01 | 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 | 3/5 (4) | 2.8/5 (4) |
Verdicts
Qwen3-VL-30B-A3B-Instruct
A mid-sized vision-language cousin for screenshots, documents, charts and video that runs fast for its size, with about 3B active parameters. It needs about 23 GB for Alibaba's Q4_K_M GGUF with its vision file, loads in every major runtime under plain Apache 2.0, and three providers host it. With no published safety work, add guardrails before it drives a GUI.
Qwen3-VL-4B-Instruct
A small vision-language cousin for reading screenshots, documents, charts and video, running in about 3.5 GB from Alibaba's Q4_K_M GGUF in every major runtime under plain Apache 2.0. It has no thinking mode, and Qwen hasn't answered repository questions or published safety work, so add your own guardrails before letting it drive a GUI.
Before you call either
Qwen3-VL-30B-A3B-Instruct
- Start it with
llama-server -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M --jinja -c 32768, orollama run qwen3-vl:30b-a3b-instruct - In Ollama the plain
qwen3-vl:30btag is the Thinking edition, so name30b-a3b-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 shipped sampling, temperature 0.7, top_p 0.8 and top_k 20, from generation_config.json
- Validate JSON output yourself, since structured output isn't documented
- vLLM needs 0.11.0 or later, Transformers 4.57.0 or later and llama.cpp b6907 or later
Qwen3-VL-4B-Instruct
- Start it with
llama-server -hf Qwen/Qwen3-VL-4B-Instruct-GGUF:Q4_K_M --jinja -c 32768, orollama run qwen3-vl:4b-instruct - In Ollama the plain
qwen3-vl:4btag is the Thinking edition, so name4b-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, Qwen3-VL-30B-A3B-Instruct or Qwen3-VL-4B-Instruct?
Qwen3-VL-30B-A3B-Instruct scores 58 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 2 of 5 scored categories.
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Machine-readable
- This page as Markdown
/compare/qwen3-vl-30b-a3b-instruct-vs-qwen3-vl-4b-instruct.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/qwen3-vl-30b-a3b-instruct.json·/api/v1/tools/qwen3-vl-4b-instruct.json - From a terminal
anchor compare qwen3-vl-30b-a3b-instruct qwen3-vl-4b-instruct(the CLI) - Over MCP
compare_tools {"a": "qwen3-vl-30b-a3b-instruct", "b": "qwen3-vl-4b-instruct"}at/mcp, no key