# GLM-4.5-Air vs MiniCPM-o 4.5

> MiniCPM-o 4.5 scores 61 (C) to GLM-4.5-Air's 59.1 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm-o-4-5
- Markdown: https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm-o-4-5.md (~6,000 tokens)
- Slim: https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm-o-4-5.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm-o-4-5.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-21

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.

- GLM-4.5-Air: grade C, 59.1/100, rank #62 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-4-5-air.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-4-5-air.json
- MiniCPM-o 4.5: grade C, 61/100, rank #42 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm-o-4-5.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm-o-4-5.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md
- Best conversational voice-agent APIs: https://www.anchorterminal.com/best/voice-agents/index.md
- All 388 voice agents comparisons: https://www.anchorterminal.com/compare/voice-agents/index.md

## Which one, for what

### GLM-4.5-Air (C)

Good 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

### MiniCPM-o 4.5 (C)

Good 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 | GLM-4.5-Air | MiniCPM-o 4.5 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 65 | 90 | MiniCPM-o 4.5 +25 |
| Risk | 20% (20 this run) | 65 | 40 | GLM-4.5-Air +25 |
| Agent interface | 10% (10 this run) | 85 | 35 | GLM-4.5-Air +50 |
| Documentation and upkeep | 10% (10 this run) | 38 | 66 | MiniCPM-o 4.5 +28 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **59.1 · C** | **61 · C** | |

## 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

1. 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
2. Use the glm45 tool and reasoning parsers for this model, the glm47 parser in Z.ai's current README is for GLM-4.7
3. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks, which makes the template add /nothink and an empty think block
4. Declare tools in OpenAI style, and the template renders arguments as arg_key and arg_value pairs that the parser turns back into JSON
5. Keep prompts under 128,000 tokens, the tokenizer config's limit, though config.json allows 131,072
6. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented for the weights

### MiniCPM-o 4.5

1. Quickest start for text and images is `ollama run openbmb/minicpm-o4.5`, about 6 GB
2. With vLLM 0.16.0 or later pass `--trust-remote-code`, and send `stop_token_ids` [151643, 151645] as OpenBMB's guide does
3. 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
4. In llama.cpp pass both the language GGUF and MiniCPM-o-4_5-vision-F16.gguf with `--mmproj`, and use `--jinja --reasoning-budget 0` to turn thinking off
5. Keep prompts within 40,960 tokens
6. 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.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm-o-4-5.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm-o-4-5.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "glm-4-5-air", "b": "minicpm-o-4-5"}`. From a terminal: `anchor compare glm-4-5-air minicpm-o-4-5`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-4-5-air.json and https://www.anchorterminal.com/api/v1/tools/minicpm-o-4-5.json

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- [MiniCPM-o 4.5 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-thinkingcap-qwen3-8-27b.md)

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