Head to head · Open-weight models you run yourself · October 2026 research run
Qwen3.5-4B vs Qwen3-VL-30B-A3B-Instruct
Qwen3.5-4B scores 61.2 (C) on agent readiness against Qwen3-VL-30B-A3B-Instruct's 58 (C), and leads in 2 of 5 scored categories. Qwen3-VL-30B-A3B-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
Qwen3.5-4BAlibaba (Qwen team) · Open-weight model
C61.2/100#33 of 100 open-weight models
Qwen3-VL-30B-A3B-InstructAlibaba (Qwen team) · Open-weight model
C58/100#68 of 100 open-weight models
Which one, for what
Best for Agents that want a cheap local helper for tool calls, short reasoning and reading images on a laptop or small GPU
Ahead on
- Capability, 53 against 39
- Agent interface, 70 against 60
Watch for
No quantised build from Alibaba for this size
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
- Reach, 95 against 85
Watch for
No safety evaluation, agent-use guidance or security contact
Score by category
| Category | Weight this run | Qwen3.5-4B | Qwen3-VL-30B-A3B-Instruct | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 53 | 39 | Qwen3.5-4B +14 |
| Reach | 25%25 | 85 | 95 | Qwen3-VL-30B-A3B-Instruct +10 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 70 | 60 | Qwen3.5-4B +10 |
| Documentation and upkeep | 10%10 | 34 | 36 | Qwen3-VL-30B-A3B-Instruct +2 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61.2/100 | C 58/100 |
Facts side by side
| Fact | Qwen3.5-4B | Qwen3-VL-30B-A3B-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 | 2026-03-02 | 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) | 3/5 (4) |
Verdicts
Qwen3.5-4B
A small vision-language cousin for tool calls, light agent steps and image or video reading on almost any machine, with switchable thinking. Ollama's 3.3 GB build or the 9.3 GB BF16 files run in every major runtime under plain Apache 2.0. The main risks are no safety results, no structured-output guarantee and no answers from Alibaba since March.
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.
Before you call either
Qwen3.5-4B
- Smallest start is
ollama run qwen3.5:4b(Q4_K_M, about 4 GB with context) - Full precision
vllm serve Qwen/Qwen3.5-4B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder - Turn thinking off with
"chat_template_kwargs": {"enable_thinking": false}in the request body, since it's on by default and/thinkand/nothinkaren't supported - There's no generation_config.json, so pass the card's sampling yourself, for example
temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5without thinking - Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens
- Validate any JSON it returns, since structured output isn't documented
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
Questions
Which is better for AI agents, Qwen3.5-4B or Qwen3-VL-30B-A3B-Instruct?
Qwen3.5-4B scores 61.2 (C) on agent readiness against Qwen3-VL-30B-A3B-Instruct's 58 (C), and leads in 2 of 5 scored categories. Qwen3-VL-30B-A3B-Instruct leads on reach.
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Machine-readable
- This page as Markdown
/compare/qwen3-5-4b-vs-qwen3-vl-30b-a3b-instruct.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/qwen3-5-4b.json·/api/v1/tools/qwen3-vl-30b-a3b-instruct.json - From a terminal
anchor compare qwen3-5-4b qwen3-vl-30b-a3b-instruct(the CLI) - Over MCP
compare_tools {"a": "qwen3-5-4b", "b": "qwen3-vl-30b-a3b-instruct"}at/mcp, no key