Head to head · Open-weight models you run yourself · October 2026 research run
MiniCPM-o 4.5 vs Qwen3-VL-2B-Instruct
MiniCPM-o 4.5 and Qwen3-VL-2B-Instruct score within a point of each other on agent readiness, 61 (C) and 61 (C). Qwen3-VL-2B-Instruct leads on reach, risk and agent interface. Both do open-weight models you run yourself.
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MiniCPM-o 4.5OpenBMB · Open-weight model
C61/100#42 of 100 open-weight models
Qwen3-VL-2B-InstructAlibaba (Qwen team) · Open-weight model
C61/100#45 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
- 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 on a laptop or small edge device that needs a local model to read images, documents and video in about 2 GB
Ahead on
- Reach, 95 against 90
- 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-2B-Instruct | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 95 | Qwen3-VL-2B-Instruct +5 |
| Risk | 20%20 | 40 | 55 | Qwen3-VL-2B-Instruct +15 |
| Agent interface | 10%10 | 35 | 60 | Qwen3-VL-2B-Instruct +25 |
| Documentation and upkeep | 10%10 | 66 | 31 | MiniCPM-o 4.5 +35 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | MiniCPM-o 4.5 | Qwen3-VL-2B-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) | 2.8/5 (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-2B-Instruct
The smallest Qwen3-VL cousin, for reading screenshots, documents, charts and video on a laptop or edge box, running in about 2 GB from Alibaba's Q4_K_M GGUF in every major runtime under plain Apache 2.0. Qwen hasn't answered repository questions or published safety work, so keep a human or a check in the loop before it drives a GUI.
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-2B-Instruct
- Start it with
llama-server -hf Qwen/Qwen3-VL-2B-Instruct-GGUF:Q4_K_M --jinja -c 32768, orollama run qwen3-vl:2b-instruct - In Ollama the plain
qwen3-vl:2btag is the Thinking edition, so name2b-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
Questions
Which is better for AI agents, MiniCPM-o 4.5 or Qwen3-VL-2B-Instruct?
MiniCPM-o 4.5 and Qwen3-VL-2B-Instruct score within a point of each other on agent readiness, 61 (C) and 61 (C). Qwen3-VL-2B-Instruct leads on reach, risk and agent interface.
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Machine-readable
- This page as Markdown
/compare/minicpm-o-4-5-vs-qwen3-vl-2b-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-2b-instruct.json - From a terminal
anchor compare minicpm-o-4-5 qwen3-vl-2b-instruct(the CLI) - Over MCP
compare_tools {"a": "minicpm-o-4-5", "b": "qwen3-vl-2b-instruct"}at/mcp, no key