Head to head · Open-weight models you run yourself · October 2026 research run
GLM-5.3 vs MiniCPM-o 4.5
GLM-5.3 scores 64.6 (B) on agent readiness against MiniCPM-o 4.5's 61 (C), and leads in 2 of 4 scored categories. MiniCPM-o 4.5 leads on reach and documentation and upkeep. 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
Best conversational voice-agent APIs · All 388 voice agents comparisons
GLM-5.3Z.ai (Zhipu AI) · Open-weight model
B64.6/100#17 of 100 open-weight models
MiniCPM-o 4.5OpenBMB · Open-weight model
C61/100#42 of 100 open-weight models
Which one, for what
GLM-5.3 B
Best for Teams with an 8-GPU server who want an open coding and long-horizon agent model with a 1M-token context, or agents that call it through Z.ai or a hosted provider
Ahead on
- Risk, 50 against 40
- Agent interface, 70 against 35
Watch for
Z.ai's smallest build is 755.6 GB in FP8, with no local Ollama or LM Studio build
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 55
- Documentation and upkeep, 66 against 58
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-5.3 | MiniCPM-o 4.5 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 55 | 90 | MiniCPM-o 4.5 +35 |
| Risk | 20%20 | 50 | 40 | GLM-5.3 +10 |
| Agent interface | 10%10 | 70 | 35 | GLM-5.3 +35 |
| Documentation and upkeep | 10%10 | 58 | 66 | MiniCPM-o 4.5 +8 |
| Negative events | ≤15 | 0 | 0 | |
| Total | B 64.6/100 | C 61/100 |
Facts side by side
| Fact | GLM-5.3 | 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 | GLM-5.3 License (MIT-style, with a security review for the largest model-as-a-service operators) | 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 | 2026-10-14 | 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-5.3
A large coding and long-horizon agent model with a 1M-token context, several tool calls per turn and three reasoning budgets. Z.ai's smallest build is 756 GB in FP8, so it needs an 8-GPU server, Z.ai's API or a provider. The main risks are no published safety work, no way to report a flaw, and thinking that can't be turned off.
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-5.3
- Start it with
vllm serve zai-org/GLM-5.3 --kv-cache-dtype fp8 --tensor-parallel-size 8 --speculative-config.method mtp --speculative-config.num_speculative_tokens 5 --tool-call-parser glm47 --reasoning-parser glm47 --enable-auto-tool-choice --served-model-name glm-5.3(vLLM 0.28.0 or later, 8xH200 or 8xH20 per vLLM's recipe) - Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; SGLang's cookbook says glm45 leaves the calls as plain text
- Pass
chat_template_kwargs: {"reasoning_effort": "low"}for short tasks; anything other than low or high falls back to max - Pass
clear_thinking: truein chat use, since the template keeps earlier reasoning by default - Don't send
enable_thinking: false; the template ignores it and always opens a think block - Validate JSON output yourself, since structured output is documented only for Z.ai's hosted API
- No Z.ai build fits a 128 GB machine; the full 1M context needs 8xB200 per vLLM's recipe
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-5.3 or MiniCPM-o 4.5?
GLM-5.3 scores 64.6 (B) on agent readiness against MiniCPM-o 4.5's 61 (C), and leads in 2 of 4 scored categories. MiniCPM-o 4.5 leads on reach and documentation and upkeep.
Other comparisons with GLM-5.3 or MiniCPM-o 4.5
- Agents-A1-4B vs GLM-5.3
- Agents-A1-4B vs MiniCPM-o 4.5
- Apertus v1.5 8B vs GLM-5.3
- Apertus v1.5 8B vs MiniCPM-o 4.5
- Apodex-1.1-mini vs GLM-5.3
- Apodex-1.1-mini vs MiniCPM-o 4.5
- BigBang-v1 vs GLM-5.3
- BigBang-v1 vs MiniCPM-o 4.5
- Cosmos-Reason2-2B vs GLM-5.3
- Cosmos-Reason2-2B vs MiniCPM-o 4.5
- Darwin-36B-Opus vs GLM-5.3
- Darwin-36B-Opus vs MiniCPM-o 4.5
- DeepSeek-V3.2 vs GLM-5.3
- DeepSeek-V3.2 vs MiniCPM-o 4.5
- DeepSeek V4.1 Flash vs GLM-5.3
- DeepSeek V4.1 Flash vs MiniCPM-o 4.5
- DeepSeek-V4-Flash-0731 vs GLM-5.3
- DeepSeek-V4-Flash-0731 vs MiniCPM-o 4.5
- DiffusionGemma 26B A4B IT vs GLM-5.3
- DiffusionGemma 26B A4B IT vs MiniCPM-o 4.5
- Gemma 4 12B IT vs GLM-5.3
- Gemma 4 12B IT vs MiniCPM-o 4.5
- Gemma 4 26B A4B IT vs GLM-5.3
- Gemma 4 26B A4B IT vs MiniCPM-o 4.5
- Gemma 4 31B IT vs GLM-5.3
- Gemma 4 31B IT vs MiniCPM-o 4.5
- Gemma 4 E2B IT vs GLM-5.3
- Gemma 4 E2B IT vs MiniCPM-o 4.5
- Gemma 4 E4B IT vs GLM-5.3
- Gemma 4 E4B IT vs MiniCPM-o 4.5
- GLM-4.5-Air vs GLM-5.3
- GLM-4.5-Air vs MiniCPM-o 4.5
- GLM-4.7-Flash vs GLM-5.3
- GLM-4.7-Flash vs MiniCPM-o 4.5
- GLM-5.3-Flash vs MiniCPM-o 4.5
- GLM-5.3 vs GLM-5.3-Flash
- GLM-5.3 vs gpt-oss-120b
- GLM-5.3 vs gpt-oss-20b
- GLM-5.3 vs Granite 4.2 3B
- GLM-5.3 vs Hy3
- GLM-5.3 vs Inkling
- GLM-5.3 vs Inkling-Small
- GLM-5.3 vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- GLM-5.3 vs Kimi K3
- GLM-5.3 vs Kolibri-1
- GLM-5.3 vs Laguna M.1
- GLM-5.3 vs Laguna S 2.1
- GLM-5.3 vs Laguna XS 2.1
- GLM-5.3 vs LFM2.5-2.6B
- GLM-5.3 vs Ling-3.0-tiny
- GLM-5.3 vs MiMo-V2.6 Distill Qwen 9B
- GLM-5.3 vs MiniCPM5-2B
- GLM-5.3 vs MiniMax-M2.5
- GLM-5.3 vs MiniMax-M2.7
- GLM-5.3 vs Muse Glimmer 30B
- GLM-5.3 vs Nemotron 3.5 Lightning 30B A3B
- GLM-5.3 vs Nemotron 3 Nano 30B A3B
- GLM-5.3 vs Nemotron 3 Nano 4B
- GLM-5.3 vs Nemotron 3 Nano Omni 30B A3B Reasoning
- GLM-5.3 vs Nemotron 3 Super 120B A12B
- GLM-5.3 vs Nemotron 3 Ultra 550B A55B
- GLM-5.3 vs occamy-1.0
- GLM-5.3 vs Olmo 3 7B Think
- GLM-5.3 vs Ornith-1.5-35B-A3B
- GLM-5.3 vs Ornith-1.5-397B
- GLM-5.3 vs Ornith-1.5-9B
- GLM-5.3 vs Qwen3-30B-A3B-Instruct-2507
- GLM-5.3 vs Qwen3-4B-Instruct-2507
- GLM-5.3 vs Qwen3.5-0.8B
- GLM-5.3 vs Qwen3.5-122B-A10B
- GLM-5.3 vs Qwen3.5-2B
- GLM-5.3 vs Qwen3.5-397B-A17B
- GLM-5.3 vs Qwen3.5-4B
- GLM-5.3 vs Qwen3.5-9B
- GLM-5.3 vs Qwen3.6-27B
- GLM-5.3 vs Qwen3.6-35B-A3B
- GLM-5.3 vs Qwen3.8-27B
- GLM-5.3 vs Qwen3.8-Flash-Next
- GLM-5.3 vs Qwen3-Coder-30B-A3B-Instruct
- GLM-5.3 vs Qwen3-Coder-Next
- GLM-5.3 vs Qwen3-Omni-30B-A3B-Instruct
- GLM-5.3 vs Qwen3-VL-2B-Instruct
- GLM-5.3 vs Qwen3-VL-30B-A3B-Instruct
- GLM-5.3 vs Qwen3-VL-32B-Instruct
- GLM-5.3 vs Qwen3-VL-4B-Instruct
- GLM-5.3 vs Qwen3-VL-8B-Instruct
- GLM-5.3 vs Qwopus3.8-27B-Flash-V2
- GLM-5.3 vs SmolLM3-3B
- GLM-5.3 vs Spark-X2.6-4B
- GLM-5.3 vs Swift 1.5 Qwen3.8-27B
- GLM-5.3 vs Swift 1.5 Qwen3.8-Flash-Next
- GLM-5.3 vs ThinkingCap (Qwen3.8 27B)
- gpt-oss-120b vs MiniCPM-o 4.5
- gpt-oss-20b vs MiniCPM-o 4.5
- Granite 4.2 3B vs MiniCPM-o 4.5
- Hy3 vs MiniCPM-o 4.5
- Inkling-Small vs MiniCPM-o 4.5
- Inkling vs MiniCPM-o 4.5
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniCPM-o 4.5
- Kimi K3 vs MiniCPM-o 4.5
- Kolibri-1 vs MiniCPM-o 4.5
- Laguna M.1 vs MiniCPM-o 4.5
- Laguna S 2.1 vs MiniCPM-o 4.5
- Laguna XS 2.1 vs MiniCPM-o 4.5
- LFM2.5-2.6B vs MiniCPM-o 4.5
- Ling-3.0-tiny vs MiniCPM-o 4.5
- MiMo-V2.6 Distill Qwen 9B vs MiniCPM-o 4.5
- MiniCPM-o 4.5 vs MiniCPM5-2B
- MiniCPM-o 4.5 vs MiniMax-M2.5
- MiniCPM-o 4.5 vs MiniMax-M2.7
- MiniCPM-o 4.5 vs Muse Glimmer 30B
- MiniCPM-o 4.5 vs Nemotron 3.5 Lightning 30B A3B
- MiniCPM-o 4.5 vs Nemotron 3 Nano 30B A3B
- MiniCPM-o 4.5 vs Nemotron 3 Nano 4B
- MiniCPM-o 4.5 vs Nemotron 3 Nano Omni 30B A3B Reasoning
- MiniCPM-o 4.5 vs Nemotron 3 Super 120B A12B
- MiniCPM-o 4.5 vs Nemotron 3 Ultra 550B A55B
- MiniCPM-o 4.5 vs occamy-1.0
- MiniCPM-o 4.5 vs Olmo 3 7B Think
- MiniCPM-o 4.5 vs Ornith-1.5-35B-A3B
- MiniCPM-o 4.5 vs Ornith-1.5-397B
- MiniCPM-o 4.5 vs Ornith-1.5-9B
- MiniCPM-o 4.5 vs Qwen3-30B-A3B-Instruct-2507
- MiniCPM-o 4.5 vs Qwen3-4B-Instruct-2507
- MiniCPM-o 4.5 vs Qwen3.5-0.8B
- MiniCPM-o 4.5 vs Qwen3.5-122B-A10B
- MiniCPM-o 4.5 vs Qwen3.5-2B
- MiniCPM-o 4.5 vs Qwen3.5-397B-A17B
- MiniCPM-o 4.5 vs Qwen3.5-4B
- MiniCPM-o 4.5 vs Qwen3.5-9B
- MiniCPM-o 4.5 vs Qwen3.6-27B
- MiniCPM-o 4.5 vs Qwen3.6-35B-A3B
- MiniCPM-o 4.5 vs Qwen3.8-27B
- MiniCPM-o 4.5 vs Qwen3.8-Flash-Next
- MiniCPM-o 4.5 vs Qwen3-Coder-30B-A3B-Instruct
- MiniCPM-o 4.5 vs Qwen3-Coder-Next
- MiniCPM-o 4.5 vs Qwen3-Omni-30B-A3B-Instruct
- MiniCPM-o 4.5 vs Qwen3-VL-2B-Instruct
- MiniCPM-o 4.5 vs Qwen3-VL-30B-A3B-Instruct
- MiniCPM-o 4.5 vs Qwen3-VL-32B-Instruct
- MiniCPM-o 4.5 vs Qwen3-VL-4B-Instruct
- MiniCPM-o 4.5 vs Qwen3-VL-8B-Instruct
- MiniCPM-o 4.5 vs Qwopus3.8-27B-Flash-V2
- MiniCPM-o 4.5 vs SmolLM3-3B
- MiniCPM-o 4.5 vs Spark-X2.6-4B
- MiniCPM-o 4.5 vs Swift 1.5 Qwen3.8-27B
- MiniCPM-o 4.5 vs Swift 1.5 Qwen3.8-Flash-Next
- MiniCPM-o 4.5 vs ThinkingCap (Qwen3.8 27B)
Machine-readable
- This page as Markdown
/compare/glm-5-3-vs-minicpm-o-4-5.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/glm-5-3.json·/api/v1/tools/minicpm-o-4-5.json - From a terminal
anchor compare glm-5-3 minicpm-o-4-5(the CLI) - Over MCP
compare_tools {"a": "glm-5-3", "b": "minicpm-o-4-5"}at/mcp, no key