# Qwen3.6-35B-A3B vs Qwen3-VL-4B-Instruct

> Qwen3.6-35B-A3B scores 63.2 (B) 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/qwen3-6-35b-a3b-vs-qwen3-vl-4b-instruct
- Markdown: https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-4b-instruct.md (~6,400 tokens)
- Slim: https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-4b-instruct.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-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

Qwen3.6-35B-A3B scores 63.2 (B) 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.

- Qwen3.6-35B-A3B: grade B, 63.2/100, rank #25 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-6-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-6-35b-a3b.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

### Qwen3.6-35B-A3B (B)

Good for: Agents with a 32 GB or larger machine that want a quick, permissively licensed coding and tool-calling cousin with vision.

Ahead on:
- Capability, 58 against 25
- Agent interface, 70 against 60
- Documentation and upkeep, 36 against 31

Watch for: No safety evaluation, no agent-use guidance and no way to report a security flaw

### 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 | Qwen3.6-35B-A3B | Qwen3-VL-4B-Instruct | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 58 | 25 | Qwen3.6-35B-A3B +33 |
| 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 | Qwen3.6-35B-A3B +10 |
| Documentation and upkeep | 10% (10 this run) | 36 | 31 | Qwen3.6-35B-A3B +5 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **63.2 · B** | **52.6 · D** | |

## Facts side by side

| Fact | Qwen3.6-35B-A3B | 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 | 2026-04-16 | 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 | 2.8/5 (4) | 2.8/5 (4) |

## Verdicts

**Qwen3.6-35B-A3B.** A fast mixture-of-experts cousin with 3B active parameters for coding and tool calling, with image and video input, in every major runtime under plain Apache 2.0. Ollama's 4-bit build needs about 28 GB. The main risk is that nobody at Alibaba maintains it, with no safety results, no security contact and no replies to tool-calling reports.

**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

### Qwen3.6-35B-A3B

1. Start it with `ollama run qwen3.6:35b` (Q4_K_M, 23 to 24 GB on disk, about 28 GB in memory)
2. On the reference machine use `vllm serve Qwen/Qwen3.6-35B-A3B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, BF16, since vLLM doesn't document FP8 compute on gfx1151
3. Use the qwen3_coder tool-call parser and the qwen3 reasoning parser in vLLM 0.19.0 or SGLang 0.5.10 or later
4. Thinking is on by default and /nothink isn't supported, so pass enable_thinking as false in chat_template_kwargs for short tasks, or preserve_thinking as true to keep earlier reasoning
5. Card sampling for thinking is temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5, and for non-thinking temperature 0.7, top_p 0.8
6. Keep at least 128K of context as the card advises, and turn on YaRN only beyond 262,144 tokens
7. Validate JSON output yourself or use the runtime's guided decoding, since the model card documents none

### 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, Qwen3.6-35B-A3B or Qwen3-VL-4B-Instruct?

Qwen3.6-35B-A3B scores 63.2 (B) 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/qwen3-6-35b-a3b-vs-qwen3-vl-4b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-4b-instruct.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "qwen3-6-35b-a3b", "b": "qwen3-vl-4b-instruct"}`. From a terminal: `anchor compare qwen3-6-35b-a3b qwen3-vl-4b-instruct`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/qwen3-6-35b-a3b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-vl-4b-instruct.json

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- [Qwen3.6-35B-A3B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-2b-instruct.md)
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