Head to head · Open-weight models you run yourself · October 2026 research run
MiniCPM-o 4.5 vs Qwen3-Coder-30B-A3B-Instruct
MiniCPM-o 4.5 scores 61 (C) on agent readiness against Qwen3-Coder-30B-A3B-Instruct's 55.7 (C), and leads in 1 of 4 scored categories. Qwen3-Coder-30B-A3B-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-Coder-30B-A3B-InstructAlibaba (Qwen team) · Open-weight model
C55.7/100#78 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
Qwen3-Coder-30B-A3B-Instruct C
Best for Coding agents and local coding tools that want a fast, capable open model on one 24 GB GPU or a 32 GB Mac, with a plain licence.
Ahead on
- Risk, 55 against 40
- Agent interface, 75 against 35
Watch for
No safety results, prompt-injection guidance or security contact, though it's meant to run commands
Score by category
| Category | Weight this run | MiniCPM-o 4.5 | Qwen3-Coder-30B-A3B-Instruct | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 90 | even |
| Risk | 20%20 | 40 | 55 | Qwen3-Coder-30B-A3B-Instruct +15 |
| Agent interface | 10%10 | 35 | 75 | Qwen3-Coder-30B-A3B-Instruct +40 |
| Documentation and upkeep | 10%10 | 66 | 31 | MiniCPM-o 4.5 +35 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 55.7/100 |
Facts side by side
| Fact | MiniCPM-o 4.5 | Qwen3-Coder-30B-A3B-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 | 2025-12-03 |
| 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-Coder-30B-A3B-Instruct
A fast coding cousin, with 3.3B of 30.5B parameters active per token, for agentic coding tools, repository reading and fill-in-the-middle without a thinking step. It needs about 21 to 23 GB at 4 bits, and several providers host it. The main risk is that it runs commands for agents with no safety results or guidance, and Alibaba doesn't answer users.
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-Coder-30B-A3B-Instruct
- Quickest start is
ollama run qwen3-coder:30b, 19 GB - On our reference machine use BF16 with
vllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder, 61 GB of weights fits the GPU share - Use the qwen3_coder parser in vLLM or SGLang, not hermes, since calls come back as function and parameter blocks rather than JSON
- Pass tool-call arguments in the history as objects, the template iterates them as a mapping
- Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and repetition_penalty 1.05, with up to 65,536 output tokens
- For code completion use the fill-in-the-middle tokens
<|fim_prefix|>,<|fim_suffix|>and<|fim_middle|>from Qwen's README - Sandbox the commands and files it touches, since nothing from Alibaba covers prompt injection
Questions
Which is better for AI agents, MiniCPM-o 4.5 or Qwen3-Coder-30B-A3B-Instruct?
MiniCPM-o 4.5 scores 61 (C) on agent readiness against Qwen3-Coder-30B-A3B-Instruct's 55.7 (C), and leads in 1 of 4 scored categories. Qwen3-Coder-30B-A3B-Instruct leads on risk and agent interface.
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Machine-readable
- This page as Markdown
/compare/minicpm-o-4-5-vs-qwen3-coder-30b-a3b-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-coder-30b-a3b-instruct.json - From a terminal
anchor compare minicpm-o-4-5 qwen3-coder-30b-a3b-instruct(the CLI) - Over MCP
compare_tools {"a": "minicpm-o-4-5", "b": "qwen3-coder-30b-a3b-instruct"}at/mcp, no key