Head to head · Open-weight models you run yourself · October 2026 research run
Qwen3.5-4B vs Swift 1.5 Qwen3.8-27B
Qwen3.5-4B and Swift 1.5 Qwen3.8-27B score within a point of each other on agent readiness, 61.2 (C) and 61 (C). Swift 1.5 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
Swift 1.5 Qwen3.8-27BUkisAI · Open-weight model
C61/100#49 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 80
- Risk, 55 against 50
- Agent interface, 70 against 60
Also in its favour
- Rated higher by the reviewer agents, 3.0 against 2.3 out of 5
Watch for
No quantised build from Alibaba for this size
Best for Agents on a 24 to 32 GB GPU or Mac that want Qwen3.8-27B's skills with shorter thinking, for teams under US$1M revenue or research.
Ahead on
- Documentation and upkeep, 43 against 34
Watch for
Commercial use above US$1M gross annual revenue needs a separate licence
Score by category
| Category | Weight this run | Qwen3.5-4B | Swift 1.5 Qwen3.8-27B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 85 | 80 | Qwen3.5-4B +5 |
| Risk | 20%20 | 55 | 50 | Qwen3.5-4B +5 |
| Agent interface | 10%10 | 70 | 60 | Qwen3.5-4B +10 |
| Documentation and upkeep | 10%10 | 34 | 43 | Swift 1.5 Qwen3.8-27B +9 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61.2/100 | C 61/100 |
Facts side by side
| Fact | Qwen3.5-4B | Swift 1.5 Qwen3.8-27B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Alibaba (Qwen team) | UkisAI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue with a separate Swift Enterprise License above it. Built on Qwen3.8-27B, whose Apache 2.0 still governs the base |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-03-02 | 2026-09-24 |
| Terms last updated | 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) | 2.3/5 (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.
Swift 1.5 Qwen3.8-27B
A Qwen3.8-27B cousin post-trained to think in fewer tokens, for coding and agent work with images in, that fits a 24 GB machine with UkisAI's own 4-bit builds. Commercial use is free only below US$1M revenue. The main risks are that nobody at UkisAI answers its discussions, and that the shorter thinking rests on UkisAI's own benchmarks.
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
Swift 1.5 Qwen3.8-27B
- Quickest start is
llama-server -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M --jinja, about 18.4 GB with the vision projector - On our reference machine BF16 fits,
vllm serve ukisai/Swift-1.5-Qwen3.8-27b --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder - The template is Qwen3.8-27B's, so
chat_template_kwargswithreasoning_effortlow, medium or xhigh (default) andenable_thinkingfalse work as on the base - UkisAI benchmarked it at temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 0, which is a reasonable starting point
- The MLX 4-bit build needs UkisAI's patched MLX-LM and has no image or video chat
- Check the licence before commercial use, it stops at US$1M gross annual revenue including affiliates
- MTP self-speculative decoding is available with the base's head, flags on the card
Questions
Which is better for AI agents, Qwen3.5-4B or Swift 1.5 Qwen3.8-27B?
Qwen3.5-4B and Swift 1.5 Qwen3.8-27B score within a point of each other on agent readiness, 61.2 (C) and 61 (C). Swift 1.5 Qwen3.8-27B leads on documentation and upkeep.
Other comparisons with Qwen3.5-4B or Swift 1.5 Qwen3.8-27B
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- Apodex-1.1-mini vs Qwen3.5-4B
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- DeepSeek-V3.2 vs Qwen3.5-4B
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- Gemma 4 26B A4B IT vs Qwen3.5-4B
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- Gemma 4 31B IT vs Qwen3.5-4B
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- Gemma 4 E2B IT vs Qwen3.5-4B
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- GLM-4.5-Air vs Qwen3.5-4B
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- GLM-4.7-Flash vs Qwen3.5-4B
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- GLM-5.3-Flash vs Qwen3.5-4B
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- GLM-5.3 vs Qwen3.5-4B
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- gpt-oss-120b vs Qwen3.5-4B
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- gpt-oss-20b vs Qwen3.5-4B
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- Granite 4.2 3B vs Qwen3.5-4B
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- Hy3 vs Qwen3.5-4B
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- Inkling-Small vs Qwen3.5-4B
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- Ornith-1.5-397B vs Qwen3.5-4B
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- Qwen3.5-4B vs Spark-X2.6-4B
- Qwen3.5-4B vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3.5-4B vs ThinkingCap (Qwen3.8 27B)
- Qwen3.5-9B vs Swift 1.5 Qwen3.8-27B
- Qwen3.6-27B vs Swift 1.5 Qwen3.8-27B
- Qwen3.6-35B-A3B vs Swift 1.5 Qwen3.8-27B
- Qwen3.8-27B vs Swift 1.5 Qwen3.8-27B
- Qwen3.8-Flash-Next vs Swift 1.5 Qwen3.8-27B
- Qwen3-Coder-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-Coder-Next vs Swift 1.5 Qwen3.8-27B
- Qwen3-Omni-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-VL-2B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-VL-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-VL-32B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-VL-4B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-VL-8B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwopus3.8-27B-Flash-V2 vs Swift 1.5 Qwen3.8-27B
- SmolLM3-3B vs Swift 1.5 Qwen3.8-27B
- Spark-X2.6-4B vs Swift 1.5 Qwen3.8-27B
- Swift 1.5 Qwen3.8-27B vs Swift 1.5 Qwen3.8-Flash-Next
- Swift 1.5 Qwen3.8-27B vs ThinkingCap (Qwen3.8 27B)
Machine-readable
- This page as Markdown
/compare/qwen3-5-4b-vs-swift-1-5-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/swift-1-5-qwen3-8-27b.json - From a terminal
anchor compare qwen3-5-4b swift-1-5-qwen3-8-27b(the CLI) - Over MCP
compare_tools {"a": "qwen3-5-4b", "b": "swift-1-5-qwen3-8-27b"}at/mcp, no key