# Qwen3.5-9B vs Qwen3-VL-4B-Instruct

> Qwen3.5-9B scores 60.5 (C) to Qwen3-VL-4B-Instruct's 52.6 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-4b-instruct
- Markdown: https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-4b-instruct.md (~6,150 tokens)
- Slim: https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-4b-instruct.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-4b-instruct.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: 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)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-21

Qwen3.5-9B scores 60.5 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 3 of 5 scored categories. Qwen3-VL-4B-Instruct leads on reach. Both do open-weight models you run yourself.

- Qwen3.5-9B: grade C, 60.5/100, rank #53 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-9b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-9b.json
- Qwen3-VL-4B-Instruct: grade D, 52.6/100, rank #84 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-vl-4b-instruct.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Qwen3.5-9B (C)

Good for: Agents that want a small multimodal tool-calling cousin on a laptop or a 16 GB GPU, under a plain licence

Ahead on:
- Capability, 51 against 25
- Agent interface, 70 against 60

Watch for: No quantised build from Qwen itself

### Qwen3-VL-4B-Instruct (D)

Good for: An agent with 4 GB or more to spare that needs a local model to read images, documents and video

Ahead on:
- Reach, 95 against 85

Watch for: No safety evaluation, agent-use guidance or security contact


## Score by category

| Category | Weight | Qwen3.5-9B | Qwen3-VL-4B-Instruct | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 51 | 25 | Qwen3.5-9B +26 |
| Reach | 25% (25 this run) | 85 | 95 | Qwen3-VL-4B-Instruct +10 |
| Risk | 20% (20 this run) | 55 | 55 | even |
| Agent interface | 10% (10 this run) | 70 | 60 | Qwen3.5-9B +10 |
| Documentation and upkeep | 10% (10 this run) | 34 | 31 | Qwen3.5-9B +3 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **60.5 · C** | **52.6 · D** | |

## Facts side by side

| Fact | Qwen3.5-9B | 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 | 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 | 2.5/5 (4) | 2.8/5 (4) |

## Verdicts

**Qwen3.5-9B.** A small dense vision-language cousin with trained tool calling, switchable thinking and a 262,144-token context, for local agent subtasks. Ollama's 4-bit build needs about 8 GB and the BF16 files about 24 GB, in every major runtime. The main risk is neglect, with no file changes since March, no replies from Qwen to reports and no safety results.

**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

### Qwen3.5-9B

1. Smallest start is `ollama run qwen3.5:9b` (6.6 GB, q4_k_m)
2. On vLLM run `vllm serve Qwen/Qwen3.5-9B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, or tool calls come back as text
3. Tool calls use the XML style `<tool_call><function=name><parameter=x>`, not Hermes JSON, so use the qwen3_coder parser and not hermes
4. Thinking is on by default; pass `chat_template_kwargs` with `enable_thinking` false for short tasks, since `/nothink` isn't supported
5. There's no generation_config.json, so pass sampling yourself, temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5 when thinking, or 0.7, 0.8, 20, 1.5 without
6. Keep at least 128K of context when thinking, and turn on YaRN only beyond 262,144 tokens
7. Validate JSON output yourself or use the runtime's guided decoding

### Qwen3-VL-4B-Instruct

1. Start it with `llama-server -hf Qwen/Qwen3-VL-4B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:4b-instruct`
2. In Ollama the plain `qwen3-vl:4b` tag is the Thinking edition, so name `4b-instruct`
3. With the GGUF, load the mmproj vision file too, or images won't be read
4. Parse tool calls as JSON inside `<tool_call>` tags, or use SGLang's `--tool-call-parser qwen`
5. Use the card's sampling, `temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5` for vision and `temperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0` for text
6. Validate JSON output yourself, since structured output isn't documented
7. vLLM needs 0.11.0 or later and Transformers 4.57.0 or later

## Questions

### Which is better for AI agents, Qwen3.5-9B or Qwen3-VL-4B-Instruct?

Qwen3.5-9B scores 60.5 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 3 of 5 scored categories. Qwen3-VL-4B-Instruct leads on reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-4b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-4b-instruct.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "qwen3-5-9b", "b": "qwen3-vl-4b-instruct"}`. From a terminal: `anchor compare qwen3-5-9b qwen3-vl-4b-instruct`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/qwen3-5-9b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-vl-4b-instruct.json

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- [Qwen3.5-9B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-2b-instruct.md)
- [Qwen3.5-9B vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-30b-a3b-instruct.md)
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- [Qwen3.5-9B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-smollm3-3b.md)
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- [Qwen3-VL-4B-Instruct vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwopus3-8-27b-flash-v2.md)
- [Qwen3-VL-4B-Instruct vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-smollm3-3b.md)
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- [Qwen3-VL-4B-Instruct vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-27b.md)
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- [Qwen3-VL-4B-Instruct vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-thinkingcap-qwen3-8-27b.md)

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