Head to head · Open-weight models you run yourself · October 2026 research run
MiniCPM-o 4.5 vs Qwen3-VL-32B-Instruct
MiniCPM-o 4.5 scores 61 (C) on agent readiness against Qwen3-VL-32B-Instruct's 56.4 (C), and leads in 2 of 4 scored categories. Qwen3-VL-32B-Instruct 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-VL-32B-InstructAlibaba (Qwen team) · Open-weight model
C56.4/100#75 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 31
Watch for
Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni
Best for An agent with a 32 GB GPU or more that wants the strongest dense Qwen3-VL model for images, documents and video
Ahead on
- Risk, 55 against 40
- Agent interface, 60 against 35
Watch for
No safety evaluation, agent-use guidance or security contact
Score by category
| Category | Weight this run | MiniCPM-o 4.5 | Qwen3-VL-32B-Instruct | 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-VL-32B-Instruct +15 |
| Agent interface | 10%10 | 35 | 60 | Qwen3-VL-32B-Instruct +25 |
| Documentation and upkeep | 10%10 | 66 | 31 | MiniCPM-o 4.5 +35 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 56.4/100 |
Facts side by side
| Fact | MiniCPM-o 4.5 | Qwen3-VL-32B-Instruct |
|---|---|---|
| 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 | 2025-11-01 |
| 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-VL-32B-Instruct
The largest dense Qwen3-VL cousin, for harder work on screenshots, documents, charts and video, needing about 25 GB from Alibaba's Q4_K_M GGUF with its vision file, in every major runtime under plain Apache 2.0. It has no thinking mode or published safety work, and only one provider was found serving it, so plan to run it yourself.
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-VL-32B-Instruct
- Start it with
llama-server -hf Qwen/Qwen3-VL-32B-Instruct-GGUF:Q4_K_M --jinja -c 32768, orollama run qwen3-vl:32b-instruct - In Ollama the plain
qwen3-vl:32btag is the Thinking edition, so name32b-instruct - With the GGUF, load the mmproj vision file too, or images won't be read
- Parse tool calls as JSON inside
<tool_call>tags, or use SGLang's--tool-call-parser qwen - Use the card's sampling,
temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5for vision andtemperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0for text - Validate JSON output yourself, since structured output isn't documented
- vLLM needs 0.11.0 or later and Transformers 4.57.0 or later
- If you pulled the FP8 build before 22 October 2025, pull it again, since the earlier files were the Thinking edition's
Questions
Which is better for AI agents, MiniCPM-o 4.5 or Qwen3-VL-32B-Instruct?
MiniCPM-o 4.5 scores 61 (C) on agent readiness against Qwen3-VL-32B-Instruct's 56.4 (C), and leads in 2 of 4 scored categories. Qwen3-VL-32B-Instruct leads on risk and agent interface.
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Machine-readable
- This page as Markdown
/compare/minicpm-o-4-5-vs-qwen3-vl-32b-instruct.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-vl-32b-instruct.json - From a terminal
anchor compare minicpm-o-4-5 qwen3-vl-32b-instruct(the CLI) - Over MCP
compare_tools {"a": "minicpm-o-4-5", "b": "qwen3-vl-32b-instruct"}at/mcp, no key