# Qwen3.5-397B-A17B vs Qwen3-VL-4B-Instruct

> Qwen3.5-397B-A17B scores 64.6 (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/qwen3-5-397b-a17b-vs-qwen3-vl-4b-instruct
- Markdown: https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-4b-instruct.md (~6,500 tokens)
- Slim: https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-4b-instruct.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-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

Qwen3.5-397B-A17B scores 64.6 (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.

- Qwen3.5-397B-A17B: grade B, 64.6/100, rank #18 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-397b-a17b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-397b-a17b.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

### Qwen3.5-397B-A17B (B)

Good for: Agents with access to an 8-GPU server, or calling a hosted endpoint, that want the strongest open Qwen3.5 with vision and a plain licence.

Ahead on:
- Capability, 72 against 25
- Agent interface, 70 against 60
- Documentation and upkeep, 39 against 31

Watch for: No build fits a 256 GB machine, and Ollama has no local build

### 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 70

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


## Score by category

| Category | Weight | Qwen3.5-397B-A17B | Qwen3-VL-4B-Instruct | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 72 | 25 | Qwen3.5-397B-A17B +47 |
| Reach | 25% (25 this run) | 70 | 95 | Qwen3-VL-4B-Instruct +25 |
| Risk | 20% (20 this run) | 55 | 55 | even |
| Agent interface | 10% (10 this run) | 70 | 60 | Qwen3.5-397B-A17B +10 |
| Documentation and upkeep | 10% (10 this run) | 39 | 31 | Qwen3.5-397B-A17B +8 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **64.6 · B** | **52.6 · D** | |

## Facts side by side

| Fact | Qwen3.5-397B-A17B | 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 | 2026-02-16 | 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

**Qwen3.5-397B-A17B.** Alibaba's biggest open Qwen3.5, a cousin for hard coding, agent and vision work. Even at 4 bits it needs about 270 GB, so most agents will call it through Alibaba's Model Studio or one of the many providers that host it. The main risk is silence, with no safety results, no agent guidance and template reports without a reply.

**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

### Qwen3.5-397B-A17B

1. The card's server command is `vllm serve Qwen/Qwen3.5-397B-A17B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, and the FP8 build halves the memory
2. It doesn't fit our 128 GB reference machine at any precision, so call a hosted endpoint instead (Model Studio's Qwen3.5-Plus, or a provider serving Qwen/Qwen3.5-397B-A17B)
3. On Model Studio pass `"enable_thinking": false` directly, while vLLM and SGLang take `"chat_template_kwargs": {"enable_thinking": false}`
4. Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without
5. For multi-token prediction in vLLM add `--speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":2}'`
6. Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens
7. Validate any JSON it returns, since structured output isn't documented

### 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, Qwen3.5-397B-A17B or Qwen3-VL-4B-Instruct?

Qwen3.5-397B-A17B scores 64.6 (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/qwen3-5-397b-a17b-vs-qwen3-vl-4b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-4b-instruct.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "qwen3-5-397b-a17b", "b": "qwen3-vl-4b-instruct"}`. From a terminal: `anchor compare qwen3-5-397b-a17b qwen3-vl-4b-instruct`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/qwen3-5-397b-a17b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-vl-4b-instruct.json

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- [Ornith-1.5-35B-A3B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-397b-a17b.md)
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- [Ornith-1.5-9B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-397b-a17b.md)
- [Ornith-1.5-9B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-4b-instruct.md)
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- [Qwen3.5-397B-A17B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-2b-instruct.md)
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- [Qwen3.5-397B-A17B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-32b-instruct.md)
- [Qwen3.5-397B-A17B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-8b-instruct.md)
- [Qwen3.5-397B-A17B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwopus3-8-27b-flash-v2.md)
- [Qwen3.5-397B-A17B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-smollm3-3b.md)
- [Qwen3.5-397B-A17B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-spark-x2-6-4b.md)
- [Qwen3.5-397B-A17B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-27b.md)
- [Qwen3.5-397B-A17B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-flash-next.md)
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- [Qwen3.6-27B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-4b-instruct.md)
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- [Qwen3.8-Flash-Next vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-4b-instruct.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)
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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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