# Qwen3-4B-Instruct-2507 vs Qwen3.5-397B-A17B

> Qwen3.5-397B-A17B scores 64.6 (B) to Qwen3-4B-Instruct-2507's 55.7 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-397b-a17b
- Markdown: https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-397b-a17b.md (~6,600 tokens)
- Slim: https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-397b-a17b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-397b-a17b.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 Qwen3-4B-Instruct-2507's 55.7 (C), and leads in 2 of 5 scored categories. Qwen3-4B-Instruct-2507 leads on reach and agent interface. Both do open-weight models you run yourself.

- Qwen3-4B-Instruct-2507: grade C, 55.7/100, rank #77 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-4b-instruct-2507.json
- 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
- 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

### Qwen3-4B-Instruct-2507 (C)

Good for: Agents that want a capable small instruct model on a laptop, phone-class device or spare GPU, with a plain licence and long context.

Ahead on:
- Reach, 95 against 70
- Agent interface, 75 against 70

Watch for: No safety results, prompt-injection guidance or security contact

### 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 31
- Documentation and upkeep, 39 against 26

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


## Score by category

| Category | Weight | Qwen3-4B-Instruct-2507 | Qwen3.5-397B-A17B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 31 | 72 | Qwen3.5-397B-A17B +41 |
| Reach | 25% (25 this run) | 95 | 70 | Qwen3-4B-Instruct-2507 +25 |
| Risk | 20% (20 this run) | 55 | 55 | even |
| Agent interface | 10% (10 this run) | 75 | 70 | Qwen3-4B-Instruct-2507 +5 |
| Documentation and upkeep | 10% (10 this run) | 26 | 39 | Qwen3.5-397B-A17B +13 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **55.7 · C** | **64.6 · B** | |

## Facts side by side

| Fact | Qwen3-4B-Instruct-2507 | Qwen3.5-397B-A17B |
| --- | --- | --- |
| 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 | 2025-09-17 | 2026-02-16 |
| 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

**Qwen3-4B-Instruct-2507.** A small, fast cousin for instruction following, short tool-calling jobs and long documents on modest hardware, with no thinking step. It runs in about 3 GB at 4 bits on every common runtime, and two providers host it. The main risk is neglect, with no file changes in over a year, no answers from Alibaba and no safety results.

**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.

## Before you call either

### Qwen3-4B-Instruct-2507

1. Quickest start is `ollama run qwen3:4b-instruct`, 2.5 GB. Plain `qwen3:4b` is the Thinking-2507 edition, not this one
2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3-4B-Instruct-2507 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser hermes`
3. Use the hermes parser in vLLM or qwen25 in SGLang, the template emits JSON inside tool_call tags
4. Don't pass enable_thinking, this edition never thinks and the card says the flag isn't needed
5. Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and min_p 0, with presence_penalty up to 2 against repetition
6. Drop the context to 32,768 tokens if memory runs out, as the card suggests
7. Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented

### 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

## Questions

### Which is better for AI agents, Qwen3-4B-Instruct-2507 or Qwen3.5-397B-A17B?

Qwen3.5-397B-A17B scores 64.6 (B) on agent readiness against Qwen3-4B-Instruct-2507's 55.7 (C), and leads in 2 of 5 scored categories. Qwen3-4B-Instruct-2507 leads on reach and agent interface.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-397b-a17b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-397b-a17b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "qwen3-4b-instruct-2507", "b": "qwen3-5-397b-a17b"}`. From a terminal: `anchor compare qwen3-4b-instruct-2507 qwen3-5-397b-a17b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/qwen3-4b-instruct-2507.json and https://www.anchorterminal.com/api/v1/tools/qwen3-5-397b-a17b.json

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- [Qwen3-4B-Instruct-2507 vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-smollm3-3b.md)
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- [Qwen3-4B-Instruct-2507 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-swift-1-5-qwen3-8-flash-next.md)
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