Head to head · Open-weight models you run yourself · October 2026 research run
Qwen3.5-0.8B vs Qwen3.5-4B
Qwen3.5-4B scores 61.2 (C) on agent readiness against Qwen3.5-0.8B's 46.7 (D), and leads in 3 of 5 scored categories. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 2870 open weights comparisons
Qwen3.5-0.8BAlibaba (Qwen team) · Open-weight model
D46.7/100#97 of 100 open-weight models
Qwen3.5-4BAlibaba (Qwen team) · Open-weight model
C61.2/100#33 of 100 open-weight models
Which one, for what
Best for Builders who want a near-free local helper or fine-tuning base for narrow tasks on a phone-class device, laptop or CPU
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
No quantised build from Alibaba and no inference provider found
Best for Agents that want a cheap local helper for tool calls, short reasoning and reading images on a laptop or small GPU
Ahead on
- Capability, 53 against 17
- Reach, 85 against 80
- Agent interface, 70 against 60
Watch for
No quantised build from Alibaba for this size
Score by category
| Category | Weight this run | Qwen3.5-0.8B | Qwen3.5-4B | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 17 | 53 | Qwen3.5-4B +36 |
| Reach | 25%25 | 80 | 85 | Qwen3.5-4B +5 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 60 | 70 | Qwen3.5-4B +10 |
| Documentation and upkeep | 10%10 | 37 | 34 | Qwen3.5-0.8B +3 |
| Negative events | ≤15 | 0 | 0 | |
| Total | D 46.7/100 | C 61.2/100 |
Facts side by side
| Fact | Qwen3.5-0.8B | Qwen3.5-4B |
|---|---|---|
| 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 | 2026-03-02 |
| 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) | 3/5 (4) |
Verdicts
Qwen3.5-0.8B
A tiny vision-language cousin for prototypes, fine-tuning and light local steps such as routing, tagging or reading an image, running in under 2 GB in every major runtime under plain Apache 2.0. Keep thinking off, since the card warns it loops. The main risk is neglect, with no hosted option, safety results or answers from Alibaba since March.
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.
Before you call either
Qwen3.5-0.8B
- Smallest start is
ollama run qwen3.5:0.8b, orqwen3.5:0.8b-q8_0at 1.0 GB - Full precision
vllm serve Qwen/Qwen3.5-0.8B --port 8000 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder - Thinking is off by default; if you turn it on with
"chat_template_kwargs": {"enable_thinking": true}, stream and cut off runaway output, since the card warns of thinking loops, and add--reasoning-parser qwen3in vLLM or SGLang to split the reasoning out - There's no generation_config.json, so pass the card's sampling yourself, for example
temperature=1.0, top_p=1.0, top_k=20, presence_penalty=2.0for text without thinking - Use it for narrow steps or as a fine-tuning base, not as the main agent, as the card itself suggests
- Validate any JSON it returns, since structured output isn't documented
Qwen3.5-4B
- Smallest start is
ollama run qwen3.5:4b(Q4_K_M, about 4 GB with context) - 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 - Turn thinking off with
"chat_template_kwargs": {"enable_thinking": false}in the request body, since it's on by default and/thinkand/nothinkaren't supported - 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=1.5without thinking - Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens
- Validate any JSON it returns, since structured output isn't documented
Questions
Which is better for AI agents, Qwen3.5-0.8B or Qwen3.5-4B?
Qwen3.5-4B scores 61.2 (C) on agent readiness against Qwen3.5-0.8B's 46.7 (D), and leads in 3 of 5 scored categories.
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Machine-readable
- This page as Markdown
/compare/qwen3-5-0-8b-vs-qwen3-5-4b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/qwen3-5-0-8b.json·/api/v1/tools/qwen3-5-4b.json - From a terminal
anchor compare qwen3-5-0-8b qwen3-5-4b(the CLI) - Over MCP
compare_tools {"a": "qwen3-5-0-8b", "b": "qwen3-5-4b"}at/mcp, no key