Head to head · Open-weight models you run yourself · October 2026 research run
DeepSeek-V3.2 vs Qwen3.5-4B
Qwen3.5-4B scores 61.2 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 3 of 5 scored categories. DeepSeek-V3.2 leads on capability. 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
DeepSeek-V3.2DeepSeek · Open-weight model
C58.6/100#65 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 Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider
Ahead on
- Capability, 69 against 53
Watch for
689.5 GB of FP8 files and no smaller build from DeepSeek or a runtime library, far beyond 107.4 GB
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 60
- 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
Score by category
| Category | Weight this run | DeepSeek-V3.2 | Qwen3.5-4B | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 69 | 53 | DeepSeek-V3.2 +16 |
| Reach | 25%25 | 60 | 85 | Qwen3.5-4B +25 |
| Risk | 20%20 | 50 | 55 | Qwen3.5-4B +5 |
| Agent interface | 10%10 | 60 | 70 | Qwen3.5-4B +10 |
| Documentation and upkeep | 10%10 | 34 | 34 | even |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 58.6/100 | C 61.2/100 |
Facts side by side
| Fact | DeepSeek-V3.2 | Qwen3.5-4B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | DeepSeek | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-12-01 | 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 | 2.3/5 (4) | 3/5 (4) |
Verdicts
DeepSeek-V3.2
DeepSeek-V3.2 is an MIT-licensed reasoning model trained for parallel tool calls, with thinking chosen per request, for long agent work. It needs about 690 GB of FP8 files, eight 80 GB GPUs in vLLM's recipe and prompts built from DeepSeek's Python. The main risk is neglect, with no file changes since December 2025 and no safety evaluation.
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
DeepSeek-V3.2
- Start it with vLLM's recipe, vllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 --tokenizer-mode deepseek_v32 --enable-auto-tool-choice --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3
- It doesn't fit the 107.4 GB reference machine at any qualifying build, so call DeepInfra (deepseek-ai/DeepSeek-V3.2) or SambaNova (DeepSeek-V3.2) instead
- Outside vLLM, build prompts with encoding/encoding_dsv32.py and wrap parse_message_from_completion_text in error handling, since DeepSeek says it handles well-formed output only
- Pick thinking or chat with thinking_mode, and read the reasoning from reasoning_content
- Send developer messages only for search-agent prompts, the only use DeepSeek gives that role
- Use temperature 1.0 and top_p 0.95 and keep prompts within 128K tokens
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, DeepSeek-V3.2 or Qwen3.5-4B?
Qwen3.5-4B scores 61.2 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 3 of 5 scored categories. DeepSeek-V3.2 leads on capability.
Other comparisons with DeepSeek-V3.2 or Qwen3.5-4B
- Agents-A1-4B vs DeepSeek-V3.2
- Agents-A1-4B vs Qwen3.5-4B
- Apertus v1.5 8B vs DeepSeek-V3.2
- Apertus v1.5 8B vs Qwen3.5-4B
- Apodex-1.1-mini vs DeepSeek-V3.2
- Apodex-1.1-mini vs Qwen3.5-4B
- BigBang-v1 vs DeepSeek-V3.2
- BigBang-v1 vs Qwen3.5-4B
- Cosmos-Reason2-2B vs DeepSeek-V3.2
- Cosmos-Reason2-2B vs Qwen3.5-4B
- Darwin-36B-Opus vs DeepSeek-V3.2
- Darwin-36B-Opus vs Qwen3.5-4B
- DeepSeek-V3.2 vs DeepSeek V4.1 Flash
- DeepSeek-V3.2 vs DeepSeek-V4-Flash-0731
- DeepSeek-V3.2 vs DiffusionGemma 26B A4B IT
- DeepSeek-V3.2 vs Gemma 4 12B IT
- DeepSeek-V3.2 vs Gemma 4 26B A4B IT
- DeepSeek-V3.2 vs Gemma 4 31B IT
- DeepSeek-V3.2 vs Gemma 4 E2B IT
- DeepSeek-V3.2 vs Gemma 4 E4B IT
- DeepSeek-V3.2 vs GLM-4.5-Air
- DeepSeek-V3.2 vs GLM-4.7-Flash
- DeepSeek-V3.2 vs GLM-5.3
- DeepSeek-V3.2 vs GLM-5.3-Flash
- DeepSeek-V3.2 vs gpt-oss-120b
- DeepSeek-V3.2 vs gpt-oss-20b
- DeepSeek-V3.2 vs Granite 4.2 3B
- DeepSeek-V3.2 vs Hy3
- DeepSeek-V3.2 vs Inkling
- DeepSeek-V3.2 vs Inkling-Small
- DeepSeek-V3.2 vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- DeepSeek-V3.2 vs Kimi K3
- DeepSeek-V3.2 vs Kolibri-1
- DeepSeek-V3.2 vs Laguna M.1
- DeepSeek-V3.2 vs Laguna S 2.1
- DeepSeek-V3.2 vs Laguna XS 2.1
- DeepSeek-V3.2 vs LFM2.5-2.6B
- DeepSeek-V3.2 vs Ling-3.0-tiny
- DeepSeek-V3.2 vs MiMo-V2.6 Distill Qwen 9B
- DeepSeek-V3.2 vs MiniCPM-o 4.5
- DeepSeek-V3.2 vs MiniCPM5-2B
- DeepSeek-V3.2 vs MiniMax-M2.5
- DeepSeek-V3.2 vs MiniMax-M2.7
- DeepSeek-V3.2 vs Muse Glimmer 30B
- DeepSeek-V3.2 vs Nemotron 3.5 Lightning 30B A3B
- DeepSeek-V3.2 vs Nemotron 3 Nano 30B A3B
- DeepSeek-V3.2 vs Nemotron 3 Nano 4B
- DeepSeek-V3.2 vs Nemotron 3 Nano Omni 30B A3B Reasoning
- DeepSeek-V3.2 vs Nemotron 3 Super 120B A12B
- DeepSeek-V3.2 vs Nemotron 3 Ultra 550B A55B
- DeepSeek-V3.2 vs occamy-1.0
- DeepSeek-V3.2 vs Olmo 3 7B Think
- DeepSeek-V3.2 vs Ornith-1.5-35B-A3B
- DeepSeek-V3.2 vs Ornith-1.5-397B
- DeepSeek-V3.2 vs Ornith-1.5-9B
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- DeepSeek-V3.2 vs Qwen3-4B-Instruct-2507
- DeepSeek-V3.2 vs Qwen3.5-0.8B
- DeepSeek-V3.2 vs Qwen3.5-122B-A10B
- DeepSeek-V3.2 vs Qwen3.5-2B
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- DeepSeek-V3.2 vs Qwen3.5-9B
- DeepSeek-V3.2 vs Qwen3.6-27B
- DeepSeek-V3.2 vs Qwen3.6-35B-A3B
- DeepSeek-V3.2 vs Qwen3.8-27B
- DeepSeek-V3.2 vs Qwen3.8-Flash-Next
- DeepSeek-V3.2 vs Qwen3-Coder-30B-A3B-Instruct
- DeepSeek-V3.2 vs Qwen3-Coder-Next
- DeepSeek-V3.2 vs Qwen3-Omni-30B-A3B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-2B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-30B-A3B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-32B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-4B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-8B-Instruct
- DeepSeek-V3.2 vs Qwopus3.8-27B-Flash-V2
- DeepSeek-V3.2 vs SmolLM3-3B
- DeepSeek-V3.2 vs Spark-X2.6-4B
- DeepSeek-V3.2 vs Swift 1.5 Qwen3.8-27B
- DeepSeek-V3.2 vs Swift 1.5 Qwen3.8-Flash-Next
- DeepSeek-V3.2 vs ThinkingCap (Qwen3.8 27B)
- DeepSeek V4.1 Flash vs Qwen3.5-4B
- DeepSeek-V4-Flash-0731 vs Qwen3.5-4B
- DiffusionGemma 26B A4B IT vs Qwen3.5-4B
- Gemma 4 12B IT vs Qwen3.5-4B
- Gemma 4 26B A4B IT vs Qwen3.5-4B
- Gemma 4 31B IT vs Qwen3.5-4B
- Gemma 4 E2B IT vs Qwen3.5-4B
- Gemma 4 E4B IT vs Qwen3.5-4B
- GLM-4.5-Air vs Qwen3.5-4B
- GLM-4.7-Flash vs Qwen3.5-4B
- GLM-5.3-Flash vs Qwen3.5-4B
- GLM-5.3 vs Qwen3.5-4B
- gpt-oss-120b vs Qwen3.5-4B
- gpt-oss-20b vs Qwen3.5-4B
- Granite 4.2 3B vs Qwen3.5-4B
- Hy3 vs Qwen3.5-4B
- Inkling-Small vs Qwen3.5-4B
- Inkling vs Qwen3.5-4B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.5-4B
- Kimi K3 vs Qwen3.5-4B
- Kolibri-1 vs Qwen3.5-4B
- Laguna M.1 vs Qwen3.5-4B
- Laguna S 2.1 vs Qwen3.5-4B
- Laguna XS 2.1 vs Qwen3.5-4B
- LFM2.5-2.6B vs Qwen3.5-4B
- Ling-3.0-tiny vs Qwen3.5-4B
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- MiniMax-M2.5 vs Qwen3.5-4B
- MiniMax-M2.7 vs Qwen3.5-4B
- Muse Glimmer 30B vs Qwen3.5-4B
- Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-4B
- Nemotron 3 Nano 30B A3B vs Qwen3.5-4B
- Nemotron 3 Nano 4B vs Qwen3.5-4B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3.5-4B
- Nemotron 3 Super 120B A12B vs Qwen3.5-4B
- Nemotron 3 Ultra 550B A55B vs Qwen3.5-4B
- occamy-1.0 vs Qwen3.5-4B
- Olmo 3 7B Think vs Qwen3.5-4B
- Ornith-1.5-35B-A3B vs Qwen3.5-4B
- Ornith-1.5-397B vs Qwen3.5-4B
- Ornith-1.5-9B vs Qwen3.5-4B
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3.5-4B
- Qwen3-4B-Instruct-2507 vs Qwen3.5-4B
- Qwen3.5-0.8B vs Qwen3.5-4B
- Qwen3.5-122B-A10B vs Qwen3.5-4B
- Qwen3.5-2B vs Qwen3.5-4B
- Qwen3.5-397B-A17B vs Qwen3.5-4B
- Qwen3.5-4B vs Qwen3.5-9B
- Qwen3.5-4B vs Qwen3.6-27B
- Qwen3.5-4B vs Qwen3.6-35B-A3B
- Qwen3.5-4B vs Qwen3.8-27B
- Qwen3.5-4B vs Qwen3.8-Flash-Next
- Qwen3.5-4B vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3.5-4B vs Qwen3-Coder-Next
- Qwen3.5-4B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3.5-4B vs Qwen3-VL-2B-Instruct
- Qwen3.5-4B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.5-4B vs Qwen3-VL-32B-Instruct
- Qwen3.5-4B vs Qwen3-VL-4B-Instruct
- Qwen3.5-4B vs Qwen3-VL-8B-Instruct
- Qwen3.5-4B vs Qwopus3.8-27B-Flash-V2
- Qwen3.5-4B vs SmolLM3-3B
- Qwen3.5-4B vs Spark-X2.6-4B
- Qwen3.5-4B vs Swift 1.5 Qwen3.8-27B
- Qwen3.5-4B vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3.5-4B vs ThinkingCap (Qwen3.8 27B)
Machine-readable
- This page as Markdown
/compare/deepseek-v3-2-vs-qwen3-5-4b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/deepseek-v3-2.json·/api/v1/tools/qwen3-5-4b.json - From a terminal
anchor compare deepseek-v3-2 qwen3-5-4b(the CLI) - Over MCP
compare_tools {"a": "deepseek-v3-2", "b": "qwen3-5-4b"}at/mcp, no key