Head to head · Open-weight models you run yourself · October 2026 research run
GLM-5.3 vs MiniCPM-V 4.7 1B
GLM-5.3 scores 64.6 (B) on agent readiness against MiniCPM-V 4.7 1B's 61 (C), and leads in 1 of 4 scored categories. MiniCPM-V 4.7 1B leads on reach and risk. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 4969 open weights comparisons
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
- Documentation and upkeep, 58 against 48
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 cheap local model to read screenshots, photos and short video on a phone, a laptop or beside a bigger model
Ahead on
- Reach, 95 against 55
- Risk, 55 against 50
Watch for
No limitations section, safety results, agent-use guidance or security contact
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Reach MiniCPM-V 4.7 1B +40
GLM-5.3 55
Memory earns nothing, since Z.ai's smallest build is the 755.6 GB FP8 one, about 907 GB with the fifth added, and Ollama only lists a cloud entry. Four runtimes run it in a released version, llama.cpp, vLLM, SGLang and Transformers, for 20, while Ollama and LM Studio have no local build. Z.ai publishes one quantised build, FP8, for 10, since the BF16 repository is the full precision. The GLM-5.3 License is a custom licence that is free for commercial use below a revenue threshold, for 10. The files download with no account or gate, for 10, and Z.ai's own API plus eight live providers on Hugging Face serve it, for 5.
MiniCPM-V 4.7 1B 95
Memory 30, OpenBMB's own IQ4_XS GGUF is 0.64 GB, 1.54 GB with the 0.90 GB vision projector, and 1.54 GB × 1.2 = 1.9 GB, which fits 16 GB with room to spare. Runtimes 25, MiniCPMV4_7ForConditionalGeneration in vLLM's registry at v0.33.0, the minicpmv4_7 model type in SGLang's minicpmv.py at v0.5.23, the minicpmv4_7 model in Transformers at v5.22.2, a MiniCPM-V 4.7 guide and conversion code in llama.cpp at b12242, and an openbmb/minicpm-v4.7:1b tag in OpenBMB's namespace on Ollama, read as the 35B-A3B listing's is. Builds 15, OpenBMB publishes AWQ, GPTQ and int4 safetensors builds and a GGUF repository from IQ3_XS to F16, so quantised builds at two precisions or more. Terms 15, Apache 2.0 in the card's metadata and as the LICENSE of OpenBMB's MiniCPM-V repository, an OSI licence. Download 10, gated is false on the main repository and on the GGUF, AWQ, GPTQ and int4 repositories, so the files download with no account or terms. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and no provider's own list was read. 30 + 25 + 15 + 15 + 10 + 0 = 95.
Documentation and upkeep GLM-5.3 +10
GLM-5.3 58
The card covers intended use, languages and context and misses limitations, architecture, parameters and cut-off, for 8. Training data is 5, a description of the GLM-5 corpus in the cited report with no cut-off. The last change, a chat template speed-up on 14 October in the FP8 repository and on 15 October in the BF16 one, is within 30 days on 2 November, for 20. Responsiveness is 5, as zai-org answered 2 of the 16 discussions that aren't pull requests, the last on 8 September, which is a few, and no discussion has been opened since 10 October. Version policy is the full 10, with each GLM-5 release in its own repository and a dated blog saying what changed from GLM-5.2. File health is the full 10, with files and card agreeing and no broken-file report waiting by title.
MiniCPM-V 4.7 1B 48
Model card 8, Intended use (image, multi-image and video understanding on device), architecture (early visual-token compression inside the vision encoder) and parameters (1B) are there. Training data 0, no description of the training data on the card. Last change 20, the last commit, on 30 October 2026, updated the card and removed an unused asset folder, eleven days before this check. Responsiveness 0, the repository has no discussions that aren't pull requests (two pull requests in all), so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 10, MiniCPM-V 4.5 and 4.6 stay in their own repositories and the GitHub README dates each release. File health 10, Config, template and card agree on the architecture and the thinking switch, and no report waits without a reply, since there are no discussions. 8 + 0 + 20 + 0 + 10 + 10 = 48.
Risk MiniCPM-V 4.7 1B +5
GLM-5.3 50
File safety is full, with safetensors only, no Python in the repository and no remote code needed in Transformers 5.12.0 or later. Provenance is full, files in the zai-org organisation Z.ai links to with a SHA-256 for every shard. Safety evaluation is 0, because the blog mentions safety evaluation and hardening but publishes no results, and agent guidance is 0, with nothing on refusals, prompt injection or cautions. Licence risk is 10, losing 5 for the security review that model-as-a-service operators above US$10 billion of revenue must pass. The security programme is 0, with no security.txt, no SECURITY.md and no advisories.
MiniCPM-V 4.7 1B 55
File safety 25, safetensors in the main repository and the AWQ, GPTQ and int4 builds, GGUF in the GGUF build, with no .bin, .pt, .pth or .py files in the main repository. Provenance 15, files in the openbmb organisation that OpenBMB's GitHub README links to (10), with a SHA-256 for both large files in the main repository (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or disclaimer section, and no prompt-injection guidance, instruction hierarchy or statement of trained refusals. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, nothing is added on the card, and the LICENSE in OpenBMB's GitHub repository resolved on 10 November 2026. Security programme 0, no SECURITY.md in OpenBMB/MiniCPM-V and no security.txt on openbmb.cn (both 404 on 10 November 2026), and no advisories found. 25 + 15 + 0 + 0 + 15 + 0 = 55.
Score by category
| Category | Weight this run | GLM-5.3 | MiniCPM-V 4.7 1B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 55 | 95 | MiniCPM-V 4.7 1B +40 |
| Risk | 20%20 | 50 | 55 | MiniCPM-V 4.7 1B +5 |
| Agent interface | 10%10 | 70 | 70 | even |
| Documentation and upkeep | 10%10 | 58 | 48 | GLM-5.3 +10 |
| Negative events | ≤15 | 0 | 0 | |
| Total | B 64.6/100 | C 61/100 |
Facts side by side
| Fact | GLM-5.3 | MiniCPM-V 4.7 1B |
|---|---|---|
| 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 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-15 | 2026-10-30 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 2.8/5 (4) | none |
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-V 4.7 1B
A vision-language cousin small enough for a phone, under plain Apache 2.0, that every runtime we count can load. The projector is bigger than the language model, so plan for about 2 GB. The card says nothing about limits or training data and doesn't show how a tool call comes back.
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-V 4.7 1B
- Start it with
llama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja, orollama run openbmb/minicpm-v4.7:1b - For image input in llama.cpp add
mmproj-model-f16.gguf, or the smaller 4x projector - The GGUF files at the repository root have thinking off. Use the files in
thinking/when you want the trace - Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends
- Check tool calls in your runtime first, since the card names no parser
- Validate JSON output yourself, since structured output isn't documented
Questions
Which is better for AI agents, GLM-5.3 or MiniCPM-V 4.7 1B?
GLM-5.3 scores 64.6 (B) on agent readiness against MiniCPM-V 4.7 1B's 61 (C), and leads in 1 of 4 scored categories. MiniCPM-V 4.7 1B leads on reach and risk.
Other comparisons with GLM-5.3 or MiniCPM-V 4.7 1B
- Agents-A1-4B vs GLM-5.3
- Agents-A1-4B vs MiniCPM-V 4.7 1B
- Apertus v1.5 8B vs GLM-5.3
- Apertus v1.5 8B vs MiniCPM-V 4.7 1B
- Apodex-1.1-mini vs GLM-5.3
- Apodex-1.1-mini vs MiniCPM-V 4.7 1B
- Arete-27B vs GLM-5.3
- Arete-27B vs MiniCPM-V 4.7 1B
- BigBang-v1 vs GLM-5.3
- BigBang-v1 vs MiniCPM-V 4.7 1B
- Carbon-X vs GLM-5.3
- Carbon-X vs MiniCPM-V 4.7 1B
- Cosmos-Reason2-2B vs GLM-5.3
- Cosmos-Reason2-2B vs MiniCPM-V 4.7 1B
- Darwin-36B-Opus vs GLM-5.3
- Darwin-36B-Opus vs MiniCPM-V 4.7 1B
- DeepSeek-V3.2 vs GLM-5.3
- DeepSeek-V3.2 vs MiniCPM-V 4.7 1B
- DeepSeek V4.1 Flash vs GLM-5.3
- DeepSeek V4.1 Flash vs MiniCPM-V 4.7 1B
- DeepSeek-V4-Flash-0731 vs GLM-5.3
- DeepSeek-V4-Flash-0731 vs MiniCPM-V 4.7 1B
- DiffusionGemma 26B A4B IT vs GLM-5.3
- DiffusionGemma 26B A4B IT vs MiniCPM-V 4.7 1B
- Gemma 4 12B IT vs GLM-5.3
- Gemma 4 12B IT vs MiniCPM-V 4.7 1B
- Gemma 4 26B A4B IT vs GLM-5.3
- Gemma 4 26B A4B IT vs MiniCPM-V 4.7 1B
- Gemma 4 31B IT vs GLM-5.3
- Gemma 4 31B IT vs MiniCPM-V 4.7 1B
- Gemma 4 E2B IT vs GLM-5.3
- Gemma 4 E2B IT vs MiniCPM-V 4.7 1B
- Gemma 4 E4B IT vs GLM-5.3
- Gemma 4 E4B IT vs MiniCPM-V 4.7 1B
- GLM-4.5-Air vs GLM-5.3
- GLM-4.5-Air vs MiniCPM-V 4.7 1B
- GLM-4.7-Flash vs GLM-5.3
- GLM-4.7-Flash vs MiniCPM-V 4.7 1B
- GLM-5.3-Flash vs MiniCPM-V 4.7 1B
- 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 30B
- GLM-5.3 vs Granite 4.2 3B
- GLM-5.3 vs Granite 4.2 8B
- GLM-5.3 vs Hy-Memory-30B-A3B
- 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 Kimi K3.1 Pro
- GLM-5.3 vs Kolibri-1
- GLM-5.3 vs Kronos
- 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 LBS-27B
- GLM-5.3 vs LFM2.5-2.6B
- GLM-5.3 vs LFM3-1.7B
- GLM-5.3 vs LFM3-Audio-2B
- GLM-5.3 vs LFM3-Omni-2B
- GLM-5.3 vs LFM3-VL-2B
- GLM-5.3 vs Ling-3.0-tiny
- GLM-5.3 vs Ling-3.1-flash-1019
- GLM-5.3 vs MiMo-V2.6 Distill Qwen 9B
- GLM-5.3 vs MiniCPM-o 4.5
- GLM-5.3 vs MiniCPM-V 4.7 35B-A3B
- GLM-5.3 vs MiniCPM5-2B
- GLM-5.3 vs MiniMax-M2.5
- GLM-5.3 vs MiniMax-M2.7
- GLM-5.3 vs MiniMax-M3.1-Flash
- GLM-5.3 vs Mistral Large 4
- GLM-5.3 vs Muse Glimmer 30B
- GLM-5.3 vs Nanbeige4.3-33B
- GLM-5.3 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- 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-1.5-122B
- 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 Qwen-AgentWorld-35B-A3B
- 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 Recursive Flint
- GLM-5.3 vs Ripple-65B Preview
- 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)
- GLM-5.3 vs Baidu Unlimited-OCR-2
- gpt-oss-120b vs MiniCPM-V 4.7 1B
- gpt-oss-20b vs MiniCPM-V 4.7 1B
- Granite 4.2 30B vs MiniCPM-V 4.7 1B
- Granite 4.2 3B vs MiniCPM-V 4.7 1B
- Granite 4.2 8B vs MiniCPM-V 4.7 1B
- Hy-Memory-30B-A3B vs MiniCPM-V 4.7 1B
- Hy3 vs MiniCPM-V 4.7 1B
- Inkling-Small vs MiniCPM-V 4.7 1B
- Inkling vs MiniCPM-V 4.7 1B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniCPM-V 4.7 1B
- Kimi K3.1 Pro vs MiniCPM-V 4.7 1B
- Kimi K3 vs MiniCPM-V 4.7 1B
- Kolibri-1 vs MiniCPM-V 4.7 1B
- Kronos vs MiniCPM-V 4.7 1B
- Laguna M.1 vs MiniCPM-V 4.7 1B
- Laguna S 2.1 vs MiniCPM-V 4.7 1B
- Laguna XS 2.1 vs MiniCPM-V 4.7 1B
- LBS-27B vs MiniCPM-V 4.7 1B
- LFM2.5-2.6B vs MiniCPM-V 4.7 1B
- LFM3-1.7B vs MiniCPM-V 4.7 1B
- LFM3-Audio-2B vs MiniCPM-V 4.7 1B
- LFM3-Omni-2B vs MiniCPM-V 4.7 1B
- LFM3-VL-2B vs MiniCPM-V 4.7 1B
- Ling-3.0-tiny vs MiniCPM-V 4.7 1B
- Ling-3.1-flash-1019 vs MiniCPM-V 4.7 1B
- MiMo-V2.6 Distill Qwen 9B vs MiniCPM-V 4.7 1B
- MiniCPM-o 4.5 vs MiniCPM-V 4.7 1B
- MiniCPM-V 4.7 1B vs MiniCPM-V 4.7 35B-A3B
- MiniCPM-V 4.7 1B vs MiniCPM5-2B
- MiniCPM-V 4.7 1B vs MiniMax-M2.5
- MiniCPM-V 4.7 1B vs MiniMax-M2.7
- MiniCPM-V 4.7 1B vs MiniMax-M3.1-Flash
- MiniCPM-V 4.7 1B vs Mistral Large 4
- MiniCPM-V 4.7 1B vs Muse Glimmer 30B
- MiniCPM-V 4.7 1B vs Nanbeige4.3-33B
- MiniCPM-V 4.7 1B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- MiniCPM-V 4.7 1B vs Nemotron 3.5 Lightning 30B A3B
- MiniCPM-V 4.7 1B vs Nemotron 3 Nano 30B A3B
- MiniCPM-V 4.7 1B vs Nemotron 3 Nano 4B
- MiniCPM-V 4.7 1B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- MiniCPM-V 4.7 1B vs Nemotron 3 Super 120B A12B
- MiniCPM-V 4.7 1B vs Nemotron 3 Ultra 550B A55B
- MiniCPM-V 4.7 1B vs occamy-1.0
- MiniCPM-V 4.7 1B vs Olmo 3 7B Think
- MiniCPM-V 4.7 1B vs Ornith-1.5-122B
- MiniCPM-V 4.7 1B vs Ornith-1.5-35B-A3B
- MiniCPM-V 4.7 1B vs Ornith-1.5-397B
- MiniCPM-V 4.7 1B vs Ornith-1.5-9B
- MiniCPM-V 4.7 1B vs Qwen-AgentWorld-35B-A3B
- MiniCPM-V 4.7 1B vs Qwen3-30B-A3B-Instruct-2507
- MiniCPM-V 4.7 1B vs Qwen3-4B-Instruct-2507
- MiniCPM-V 4.7 1B vs Qwen3.5-0.8B
- MiniCPM-V 4.7 1B vs Qwen3.5-122B-A10B
- MiniCPM-V 4.7 1B vs Qwen3.5-2B
- MiniCPM-V 4.7 1B vs Qwen3.5-397B-A17B
- MiniCPM-V 4.7 1B vs Qwen3.5-4B
- MiniCPM-V 4.7 1B vs Qwen3.5-9B
- MiniCPM-V 4.7 1B vs Qwen3.6-27B
- MiniCPM-V 4.7 1B vs Qwen3.6-35B-A3B
- MiniCPM-V 4.7 1B vs Qwen3.8-27B
- MiniCPM-V 4.7 1B vs Qwen3.8-Flash-Next
- MiniCPM-V 4.7 1B vs Qwen3-Coder-30B-A3B-Instruct
- MiniCPM-V 4.7 1B vs Qwen3-Coder-Next
- MiniCPM-V 4.7 1B vs Qwen3-Omni-30B-A3B-Instruct
- MiniCPM-V 4.7 1B vs Qwen3-VL-2B-Instruct
- MiniCPM-V 4.7 1B vs Qwen3-VL-30B-A3B-Instruct
- MiniCPM-V 4.7 1B vs Qwen3-VL-32B-Instruct
- MiniCPM-V 4.7 1B vs Qwen3-VL-4B-Instruct
- MiniCPM-V 4.7 1B vs Qwen3-VL-8B-Instruct
- MiniCPM-V 4.7 1B vs Qwopus3.8-27B-Flash-V2
- MiniCPM-V 4.7 1B vs Recursive Flint
- MiniCPM-V 4.7 1B vs Ripple-65B Preview
- MiniCPM-V 4.7 1B vs SmolLM3-3B
- MiniCPM-V 4.7 1B vs Spark-X2.6-4B
- MiniCPM-V 4.7 1B vs Swift 1.5 Qwen3.8-27B
- MiniCPM-V 4.7 1B vs Swift 1.5 Qwen3.8-Flash-Next
- MiniCPM-V 4.7 1B vs ThinkingCap (Qwen3.8 27B)
- MiniCPM-V 4.7 1B vs Baidu Unlimited-OCR-2
Machine-readable
- This page as Markdown
/compare/glm-5-3-vs-minicpm-v-4-7-1b.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-v-4-7-1b.json - From a terminal
anchor compare glm-5-3 minicpm-v-4-7-1b(the CLI) - Over MCP
compare_tools {"a": "glm-5-3", "b": "minicpm-v-4-7-1b"}at/mcp, no key