Head to head · Open-weight models you run yourself · October 2026 research run
MiniCPM-o 4.5 vs Qwen3-Coder-Next
MiniCPM-o 4.5 scores 61 (C) on agent readiness against Qwen3-Coder-Next's 57.6 (C), and leads in 2 of 4 scored categories. Qwen3-Coder-Next leads on 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-Coder-NextAlibaba (Qwen team) · Open-weight model
C57.6/100#70 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 41
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 local coding model for long tool-calling loops on a single 64 GB to 128 GB machine.
Ahead on
- Risk, 50 against 40
- Agent interface, 75 against 35
Watch for
No LICENSE file anywhere and a broken licence link on the card
Score by category
| Category | Weight this run | MiniCPM-o 4.5 | Qwen3-Coder-Next | 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 | 50 | Qwen3-Coder-Next +10 |
| Agent interface | 10%10 | 35 | 75 | Qwen3-Coder-Next +40 |
| Documentation and upkeep | 10%10 | 66 | 41 | MiniCPM-o 4.5 +25 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 57.6/100 |
Facts side by side
| Fact | MiniCPM-o 4.5 | Qwen3-Coder-Next |
|---|---|---|
| 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 | 2026-02-03 |
| 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-Next
A fast coding cousin for agent harnesses, with 3B active parameters and tool calling trained across many scaffold formats for Claude Code, Cline or Qwen Code loops. Alibaba's 4-bit GGUF needs about 58 GB, fitting one 64 GB machine, and two providers host it. The main risk is neglect, with no change since February, no replies 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-Coder-Next
- Quickest start is
ollama run qwen3-coder-next(Q4_K_M, 52 GB of files) - On our reference machine Alibaba's Q8_0 GGUF (84.8 GB, about 102 GB in use) also fits, run with
llama-server -m Qwen3-Coder-Next-Q8_0/Qwen3-Coder-Next-Q8_0-00001-of-00004.gguf --jinja -ngl 99 -fa on -c 131072 - Serve with
vllm serve Qwen/Qwen3-Coder-Next --enable-auto-tool-choice --tool-call-parser qwen3_coder(vLLM 0.15.0 or later), using the built-in parser rather than the repository's qwen3coder_tool_parser_vllm.py, which fails to import on newer vLLM per discussion 36 - The card's commands use tensor parallel 2 though the text says 4 GPUs, and it suggests cutting the context to 32,768 if the server fails to start
- There's no thinking mode, so don't pass
enable_thinkingor wait for reasoning_content - Sampling per the card is temperature 1.0, top_p 0.95 and top_k 40
- The GGUF card's llama.cpp example path omits the quant from the file name; the real files are named like Qwen3-Coder-Next-Q4_K_M-00001-of-00004.gguf
- Validate any JSON it returns, since structured output isn't documented
Questions
Which is better for AI agents, MiniCPM-o 4.5 or Qwen3-Coder-Next?
MiniCPM-o 4.5 scores 61 (C) on agent readiness against Qwen3-Coder-Next's 57.6 (C), and leads in 2 of 4 scored categories. Qwen3-Coder-Next leads on risk and agent interface.
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Machine-readable
- This page as Markdown
/compare/minicpm-o-4-5-vs-qwen3-coder-next.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-next.json - From a terminal
anchor compare minicpm-o-4-5 qwen3-coder-next(the CLI) - Over MCP
compare_tools {"a": "minicpm-o-4-5", "b": "qwen3-coder-next"}at/mcp, no key