Head to head · Open-weight models you run yourself · October 2026 research run
GLM-4.5-Air vs MiniCPM-o 4.5
MiniCPM-o 4.5 scores 61 (C) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 2 of 4 scored categories. GLM-4.5-Air leads on risk and agent interface. Both do open-weight models you run yourself.
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GLM-4.5-AirZ.ai (Zhipu AI) · Open-weight model
C59.1/100#62 of 100 open-weight models
MiniCPM-o 4.5OpenBMB · Open-weight model
C61/100#42 of 100 open-weight models
Which one, for what
Best for Agents with two to four datacentre GPUs, or a provider account, who want a permissively licensed English and Chinese tool-calling model with wide runtime support.
Ahead on
- Risk, 65 against 40
- Agent interface, 85 against 35
Watch for
Z.ai's smallest build is FP8 at about 135 GB in use, with no 4-bit build of its own
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 65
- Documentation and upkeep, 66 against 38
Watch for
Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni
Score by category
| Category | Weight this run | GLM-4.5-Air | MiniCPM-o 4.5 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 65 | 90 | MiniCPM-o 4.5 +25 |
| Risk | 20%20 | 65 | 40 | GLM-4.5-Air +25 |
| Agent interface | 10%10 | 85 | 35 | GLM-4.5-Air +50 |
| Documentation and upkeep | 10%10 | 38 | 66 | MiniCPM-o 4.5 +28 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 59.1/100 | C 61/100 |
Facts side by side
| Fact | GLM-4.5-Air | MiniCPM-o 4.5 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Z.ai (Zhipu AI) | OpenBMB |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Apache-2.0; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-08-11 | 2026-10-16 |
| 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
GLM-4.5-Air
An English and Chinese cousin for agent, coding and tool-calling work, with switchable thinking and wide runtime support. It needs about 135 GB for Z.ai's FP8 build, two to four H100s by Z.ai's figures, and Z.ai and Novita host it. The main risk is age, with files from August 2025 and no answers from Z.ai since.
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.
Before you call either
GLM-4.5-Air
- Z.ai's FP8 recipe is
python3 -m sglang.launch_server --model-path zai-org/GLM-4.5-Air-FP8 --tp-size 4 --tool-call-parser glm45 --reasoning-parser glm45, and 2 H100s are enough below the full 128K context - Use the glm45 tool and reasoning parsers for this model, the glm47 parser in Z.ai's current README is for GLM-4.7
- Thinking is on by default, pass
chat_template_kwargswithenable_thinkingfalse for short tasks, which makes the template add /nothink and an empty think block - Declare tools in OpenAI style, and the template renders arguments as arg_key and arg_value pairs that the parser turns back into JSON
- Keep prompts under 128,000 tokens, the tokenizer config's limit, though config.json allows 131,072
- Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented for the weights
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
Questions
Which is better for AI agents, GLM-4.5-Air or MiniCPM-o 4.5?
MiniCPM-o 4.5 scores 61 (C) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 2 of 4 scored categories. GLM-4.5-Air leads on risk and agent interface.
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Machine-readable
- This page as Markdown
/compare/glm-4-5-air-vs-minicpm-o-4-5.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/glm-4-5-air.json·/api/v1/tools/minicpm-o-4-5.json - From a terminal
anchor compare glm-4-5-air minicpm-o-4-5(the CLI) - Over MCP
compare_tools {"a": "glm-4-5-air", "b": "minicpm-o-4-5"}at/mcp, no key