Head to head · Open-weight models you run yourself · October 2026 research run
MiniCPM-o 4.5 vs Qwen3-30B-A3B-Instruct-2507
MiniCPM-o 4.5 scores 61 (C) on agent readiness against Qwen3-30B-A3B-Instruct-2507's 56.6 (C), and leads in 1 of 4 scored categories. Qwen3-30B-A3B-Instruct-2507 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
Qwen3-30B-A3B-Instruct-2507Alibaba (Qwen team) · Open-weight model
C56.6/100#74 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 26
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 fast, capable instruct model on one 24 GB GPU or a 32 GB Mac, with a plain licence and long context.
Ahead on
- Risk, 55 against 40
- Agent interface, 75 against 35
Watch for
No safety results, prompt-injection guidance or security contact
Score by category
| Category | Weight this run | MiniCPM-o 4.5 | Qwen3-30B-A3B-Instruct-2507 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 90 | even |
| Risk | 20%20 | 40 | 55 | Qwen3-30B-A3B-Instruct-2507 +15 |
| Agent interface | 10%10 | 35 | 75 | Qwen3-30B-A3B-Instruct-2507 +40 |
| Documentation and upkeep | 10%10 | 66 | 26 | MiniCPM-o 4.5 +40 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 56.6/100 |
Facts side by side
| Fact | MiniCPM-o 4.5 | Qwen3-30B-A3B-Instruct-2507 |
|---|---|---|
| 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 | 2025-09-17 |
| 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.
Qwen3-30B-A3B-Instruct-2507
A quick general-purpose cousin, with 3.3B of 30.5B parameters active per token, for instruction following, tool calling and long documents without a thinking step. It needs about 21 to 23 GB at 4 bits, and several providers host it. The main risk is neglect, with no file changes in over a year, no answers from Alibaba 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
Qwen3-30B-A3B-Instruct-2507
- Quickest start is
ollama run qwen3:30b-instruct, 19 GB. Plainqwen3:30bandqwen3:30b-a3bare the Thinking-2507 edition, not this one - On our reference machine use BF16 with
vllm serve Qwen/Qwen3-30B-A3B-Instruct-2507 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser hermes, 61 GB of weights fits the GPU share - Use the hermes parser in vLLM or qwen25 in SGLang, the template emits JSON inside tool_call tags
- Don't pass enable_thinking, this edition never thinks and the card says the flag isn't needed
- Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and min_p 0, with presence_penalty up to 2 against repetition
- Leave config.json as it is unless you need more than 262,144 tokens, the 1M mode swaps in config_1m.json and runs one sequence at a time
- Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented
Questions
Which is better for AI agents, MiniCPM-o 4.5 or Qwen3-30B-A3B-Instruct-2507?
MiniCPM-o 4.5 scores 61 (C) on agent readiness against Qwen3-30B-A3B-Instruct-2507's 56.6 (C), and leads in 1 of 4 scored categories. Qwen3-30B-A3B-Instruct-2507 leads on risk and agent interface.
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Machine-readable
- This page as Markdown
/compare/minicpm-o-4-5-vs-qwen3-30b-a3b-instruct-2507.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/qwen3-30b-a3b-instruct-2507.json - From a terminal
anchor compare minicpm-o-4-5 qwen3-30b-a3b-instruct-2507(the CLI) - Over MCP
compare_tools {"a": "minicpm-o-4-5", "b": "qwen3-30b-a3b-instruct-2507"}at/mcp, no key