Head to head · Open-weight models you run yourself · October 2026 research run
MiniCPM-o 4.5 vs Qwen3.5-9B
MiniCPM-o 4.5 and Qwen3.5-9B score within a point of each other on agent readiness, 61 (C) and 60.5 (C). Qwen3.5-9B leads on risk and agent interface. 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
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MiniCPM-o 4.5OpenBMB · Open-weight model
C61/100#42 of 100 open-weight models
Qwen3.5-9BAlibaba (Qwen team) · Open-weight model
C60.5/100#53 of 100 open-weight models
Which one, for what
Best for Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.
Ahead on
- Reach, 90 against 85
- Documentation and upkeep, 66 against 34
Watch for
Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni
Best for Agents that want a small multimodal tool-calling cousin on a laptop or a 16 GB GPU, under a plain licence
Ahead on
- Risk, 55 against 40
- Agent interface, 70 against 35
Watch for
No quantised build from Qwen itself
Score by category
| Category | Weight this run | MiniCPM-o 4.5 | Qwen3.5-9B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 85 | MiniCPM-o 4.5 +5 |
| Risk | 20%20 | 40 | 55 | Qwen3.5-9B +15 |
| Agent interface | 10%10 | 35 | 70 | Qwen3.5-9B +35 |
| Documentation and upkeep | 10%10 | 66 | 34 | MiniCPM-o 4.5 +32 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 60.5/100 |
Facts side by side
| Fact | MiniCPM-o 4.5 | Qwen3.5-9B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | OpenBMB | 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; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-16 | 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) | 2.5/5 (4) |
Verdicts
MiniCPM-o 4.5
A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results.
Qwen3.5-9B
A small dense vision-language cousin with trained tool calling, switchable thinking and a 262,144-token context, for local agent subtasks. Ollama's 4-bit build needs about 8 GB and the BF16 files about 24 GB, in every major runtime. The main risk is neglect, with no file changes since March, no replies from Qwen to reports and no safety results.
Before you call either
MiniCPM-o 4.5
- Quickest start for text and images is
ollama run openbmb/minicpm-o4.5, about 6 GB - With vLLM 0.16.0 or later pass
--trust-remote-code, and sendstop_token_ids[151643, 151645] as OpenBMB's guide does - vLLM, llama.cpp and Ollama return text only; for speech out use the card's Transformers 4.51.0 code or OpenBMB's llama.cpp-omni
- In llama.cpp pass both the language GGUF and MiniCPM-o-4_5-vision-F16.gguf with
--mmproj, and use--jinja --reasoning-budget 0to turn thinking off - Keep prompts within 40,960 tokens
- Don't rely on tool calls or JSON output, neither is documented; validate any you get
Qwen3.5-9B
- Smallest start is
ollama run qwen3.5:9b(6.6 GB, q4_k_m) - On vLLM run
vllm serve Qwen/Qwen3.5-9B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, or tool calls come back as text - Tool calls use the XML style
<tool_call><function=name><parameter=x>, not Hermes JSON, so use the qwen3_coder parser and not hermes - Thinking is on by default; pass
chat_template_kwargswithenable_thinkingfalse for short tasks, since/nothinkisn't supported - There's no generation_config.json, so pass sampling yourself, temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5 when thinking, or 0.7, 0.8, 20, 1.5 without
- Keep at least 128K of context when thinking, and turn on YaRN only beyond 262,144 tokens
- Validate JSON output yourself or use the runtime's guided decoding
Questions
Which is better for AI agents, MiniCPM-o 4.5 or Qwen3.5-9B?
MiniCPM-o 4.5 and Qwen3.5-9B score within a point of each other on agent readiness, 61 (C) and 60.5 (C). Qwen3.5-9B leads on risk and agent interface.
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Machine-readable
- This page as Markdown
/compare/minicpm-o-4-5-vs-qwen3-5-9b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/minicpm-o-4-5.json·/api/v1/tools/qwen3-5-9b.json - From a terminal
anchor compare minicpm-o-4-5 qwen3-5-9b(the CLI) - Over MCP
compare_tools {"a": "minicpm-o-4-5", "b": "qwen3-5-9b"}at/mcp, no key