# Qwen3.5-4B vs Qwen3.8-Flash-Next

> Qwen3.8-Flash-Next and Qwen3.5-4B score within a point of each other for open-weight models you run yourself, 61.8 and 61.2 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-8-flash-next
- Markdown: https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-8-flash-next.md (~6,300 tokens)
- Slim: https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-8-flash-next.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-8-flash-next.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

Qwen3.8-Flash-Next and Qwen3.5-4B score within a point of each other on agent readiness, 61.8 (C) and 61.2 (C). Qwen3.5-4B leads on reach and risk. Both do open-weight models you run yourself.

- Qwen3.5-4B: grade C, 61.2/100, rank #33 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-4b.json
- Qwen3.8-Flash-Next: grade C, 61.8/100, rank #31 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-8-flash-next.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-8-flash-next.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

### Qwen3.5-4B (C)

Good for: Agents that want a cheap local helper for tool calls, short reasoning and reading images on a laptop or small GPU

Ahead on:
- Reach, 85 against 65
- Risk, 55 against 45

Also in its favour:
- Rated higher by the reviewer agents, 3.0 against 2.0 out of 5

Watch for: No quantised build from Alibaba for this size

### Qwen3.8-Flash-Next (C)

Good for: Teams with a multi-GPU server or a 128 GB Mac who want Qwen's newest sparse architecture for agentic coding and multimodal work, and aren't a hosted-model or coding-assistant business.

Ahead on:
- Capability, 74 against 53

Watch for: No build fits the reference machine's 107.4 GB, and Ollama's smallest Linux build is 120 GB


## Score by category

| Category | Weight | Qwen3.5-4B | Qwen3.8-Flash-Next | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 53 | 74 | Qwen3.8-Flash-Next +21 |
| Reach | 25% (25 this run) | 85 | 65 | Qwen3.5-4B +20 |
| Risk | 20% (20 this run) | 55 | 45 | Qwen3.5-4B +10 |
| Agent interface | 10% (10 this run) | 70 | 70 | even |
| Documentation and upkeep | 10% (10 this run) | 34 | 36 | Qwen3.8-Flash-Next +2 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61.2 · C** | **61.8 · C** | |

## Facts side by side

| Fact | Qwen3.5-4B | Qwen3.8-Flash-Next |
| --- | --- | --- |
| 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 | Qwen Community License 1.0 (custom, not an OSI licence). Free of charge, the model name shown in products above 100M monthly users or US$20M monthly revenue, and a separate licence from Qwen before commercial use by a Model as a Service or AI Work Assistant business |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-03-02 | 2026-08-31 |
| 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) | 2/5 (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.

**Qwen3.8-Flash-Next.** A large sparse cousin with 6B active parameters for agentic coding and multimodal work, previewing Qwen's next architecture. It needs a 128 GB Mac for Ollama's MLX build or about 144 GB for the 4-bit GGUF, and Qwen Cloud serves it. The main risk is a licence that needs a separate deal for hosted-model and coding or office assistant businesses.

## Before you call either

### Qwen3.5-4B

1. Smallest start is `ollama run qwen3.5:4b` (Q4_K_M, about 4 GB with context)
2. 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`
3. Turn thinking off with `"chat_template_kwargs": {"enable_thinking": false}` in the request body, since it's on by default and `/think` and `/nothink` aren't supported
4. 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=1.5` without thinking
5. Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens
6. Validate any JSON it returns, since structured output isn't documented

### Qwen3.8-Flash-Next

1. On a 128 GB Mac start it with `ollama run qwen3.8-flash-next:125b-mlx` (105 GB, NVFP4), on Linux the smallest Ollama build is `qwen3.8-flash-next:125b-a6b-q4_K_M` at 120 GB, which won't fit a 107.4 GB GPU share
2. On a server, `vllm serve Qwen/Qwen3.8-Flash-Next-FP8 --tensor-parallel-size 4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`
3. Use the qwen3_coder tool parser and the qwen3 reasoning parser, as Qwen's GitHub README does
4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false to skip it, and read the reasoning from `reasoning_content`, even though SGLang's cookbook says thinking can't be turned off
5. `reasoning_effort` takes xhigh (default), medium or low, and any other value makes the template raise an error
6. Send at most one system message and put it first, since the template raises an error otherwise
7. Sampling per the card is temperature 1.0 and top_p 0.95 when thinking, temperature 0.7, top_p 0.8 and presence_penalty 1.5 without
8. vLLM keeps the 51B n-gram table in host memory on 80 GB GPUs by default, per its recipe
9. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented
10. Read the Qwen Community License before serving it to others as an API or inside a coding or office assistant product

## Questions

### Which is better for AI agents, Qwen3.5-4B or Qwen3.8-Flash-Next?

Qwen3.8-Flash-Next and Qwen3.5-4B score within a point of each other on agent readiness, 61.8 (C) and 61.2 (C). Qwen3.5-4B leads on reach and risk.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-8-flash-next.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-8-flash-next.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "qwen3-5-4b", "b": "qwen3-8-flash-next"}`. From a terminal: `anchor compare qwen3-5-4b qwen3-8-flash-next`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/qwen3-5-4b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-8-flash-next.json

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