Head to head · Open-weight models you run yourself · October 2026 research run
Qwen3.5-4B vs ThinkingCap (Qwen3.8 27B)
Qwen3.5-4B and ThinkingCap (Qwen3.8 27B) score within a point of each other on agent readiness, 61.2 (C) and 61 (C). ThinkingCap (Qwen3.8 27B) leads on documentation and upkeep. 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
Qwen3.5-4BAlibaba (Qwen team) · Open-weight model
C61.2/100#33 of 100 open-weight models
ThinkingCap (Qwen3.8 27B)BottleCap AI · Open-weight model
C61/100#50 of 100 open-weight models
Which one, for what
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
- Reach, 85 against 75
- Risk, 55 against 45
Also in its favour
- Rated higher by the reviewer agents, 3.0 against 2.5 out of 5
Watch for
No quantised build from Alibaba for this size
Best for Small teams and individuals running Qwen3.8-27B for agent or reasoning work who want shorter thinking traces on one 24 GB GPU or Mac.
Ahead on
- Documentation and upkeep, 53 against 34
Watch for
PolyForm Small Business licence, free commercially only below 100 people and 1,000,000 USD (2019) revenue
Score by category
| Category | Weight this run | Qwen3.5-4B | ThinkingCap (Qwen3.8 27B) | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 85 | 75 | Qwen3.5-4B +10 |
| Risk | 20%20 | 55 | 45 | Qwen3.5-4B +10 |
| Agent interface | 10%10 | 70 | 70 | even |
| Documentation and upkeep | 10%10 | 34 | 53 | ThinkingCap (Qwen3.8 27B) +19 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61.2/100 | C 61/100 |
Facts side by side
| Fact | Qwen3.5-4B | ThinkingCap (Qwen3.8 27B) |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Alibaba (Qwen team) | BottleCap AI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | PolyForm-Small-Business-1.0.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-03-02 | 2026-10-06 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | 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) | 2.5/5 (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.
ThinkingCap (Qwen3.8 27B)
A Qwen3.8-27B with shorter reasoning, for agent work where thinking tokens cost time, and BottleCap's 4-bit GGUF runs in about 19 GB. The main limits are the licence, which needs a paid licence above 100 people or 1,000,000 USD revenue, a gated main repository, and no safety results or training-data description.
Before you call either
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
ThinkingCap (Qwen3.8 27B)
- Check the licence first, an organisation with 100 or more people or 1,000,000 USD (2019) revenue needs a commercial licence from BottleCap
- For files without the gate use
llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja, about 17.4 GB - On vLLM use
--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml, and on SGLang--tool-call-parser qwen3_coder - Set
reasoning_effort(xhigh, medium or low) in chat_template_kwargs, any other value makes the template raise an error - Keep the base's sampling (temperature 1.0, top_p 0.95, top_k 20), the GGUF card says greedy decoding can loop
- The GGUFs carry an MTP head, and llama.cpp builds without MTP support for this architecture may refuse to load them with a missing blk.64 tensor
Questions
Which is better for AI agents, Qwen3.5-4B or ThinkingCap (Qwen3.8 27B)?
Qwen3.5-4B and ThinkingCap (Qwen3.8 27B) score within a point of each other on agent readiness, 61.2 (C) and 61 (C). ThinkingCap (Qwen3.8 27B) leads on documentation and upkeep.
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Machine-readable
- This page as Markdown
/compare/qwen3-5-4b-vs-thinkingcap-qwen3-8-27b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/qwen3-5-4b.json·/api/v1/tools/thinkingcap-qwen3-8-27b.json - From a terminal
anchor compare qwen3-5-4b thinkingcap-qwen3-8-27b(the CLI) - Over MCP
compare_tools {"a": "qwen3-5-4b", "b": "thinkingcap-qwen3-8-27b"}at/mcp, no key