Head to head · Open-weight models you run yourself · October 2026 research run
GLM-5.3 vs WeVisOne-VL-4B
GLM-5.3 scores 64.6 (B) on agent readiness against WeVisOne-VL-4B's 61 (C), and leads in 1 of 4 scored categories. WeVisOne-VL-4B 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 6359 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 to locate, count or cut out objects in images on a small GPU, under a permissive licence
Ahead on
- Reach, 70 against 55
- Risk, 55 against 50
Watch for
The pixel decoder ships as a pickle file
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 WeVisOne-VL-4B +15
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.
WeVisOne-VL-4B 70
Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 9.7 GB, and 9.7 GB × 1.2 = 11.6 GB, within 16 GB. No runtime library has a build. Runtimes 15, the architecture is Qwen3VLForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors file, three at 5 each. Builds 0, Tencent publishes the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata with the LICENSE file in the repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 30 + 15 + 0 + 15 + 10 + 0 = 70.
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.
WeVisOne-VL-4B 48
Model card 8, Intended use, limitations and the architecture are on the card. Training data 5, A general description in the README of a data engine and over 100 million annotated images, with no sources named and no cut-off. Last change 20, the last commit, on 27 October 2026, added links to the paper and project page, 14 days before this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (23 and 26 October 2026) has a reply from a tencent member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, 14 days old. File health 10, Config, template and card agree on the Qwen3-VL classes, and the card names mask_decoder.pt as required and it's there. 8 + 5 + 20 + 0 + 5 + 10 = 48.
Risk WeVisOne-VL-4B +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.
WeVisOne-VL-4B 55
File safety 15, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it. Provenance 15, the files sit in the tencent organisation, which Tencent's GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card or in the README. Agent guidance 5, General cautions: the card says the release is for research, isn't for autonomous high-impact decisions or identifying individuals, and lists where masks go wrong. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide, as the Hy3 listing records (5). 15 + 15 + 0 + 5 + 15 + 5 = 55.
Score by category
| Category | Weight this run | GLM-5.3 | WeVisOne-VL-4B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 55 | 70 | WeVisOne-VL-4B +15 |
| Risk | 20%20 | 50 | 55 | WeVisOne-VL-4B +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 | WeVisOne-VL-4B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Z.ai (Zhipu AI) | Tencent (WeChat Vision) |
| 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 | 2026-10-27 |
| Terms last updated | no document linked | not read yet |
| 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.
WeVisOne-VL-4B
A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet 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
WeVisOne-VL-4B
- Use Tencent's package:
WeVisOneVL("tencent/WeVisOne-VL-4B", backend="vllm")thenmodel.predict(image, task=..., query=...) - Tasks are detection, referring, point, count_box, count_point, ocr, ocr_grounding, segmentation and matting
- A None answer means the target isn't in the image. Treat it as a result, not an error
- mask_decoder.pt is a pickle. Load it only from the tencent repository, and check its SHA-256 against the Hub's
- Send one image a turn. Video grids are rejected by the decoder
- The card says it isn't for identifying individuals or for high-impact decisions on its own
Questions
Which is better for AI agents, GLM-5.3 or WeVisOne-VL-4B?
GLM-5.3 scores 64.6 (B) on agent readiness against WeVisOne-VL-4B's 61 (C), and leads in 1 of 4 scored categories. WeVisOne-VL-4B leads on reach and risk.
Other comparisons with GLM-5.3 or WeVisOne-VL-4B
- Agents-A1-4B vs GLM-5.3
- Agents-A1-4B vs WeVisOne-VL-4B
- Apertus v1.5 8B vs GLM-5.3
- Apertus v1.5 8B vs WeVisOne-VL-4B
- Apodex-1.1-mini vs GLM-5.3
- Apodex-1.1-mini vs WeVisOne-VL-4B
- Arete-27B vs GLM-5.3
- Arete-27B vs WeVisOne-VL-4B
- BigBang-v1 vs GLM-5.3
- BigBang-v1 vs WeVisOne-VL-4B
- Carbon-X vs GLM-5.3
- Carbon-X vs WeVisOne-VL-4B
- Cola2 vs GLM-5.3
- Cola2 vs WeVisOne-VL-4B
- Cosmos-Reason2-2B vs GLM-5.3
- Cosmos-Reason2-2B vs WeVisOne-VL-4B
- Darwin-36B-Opus vs GLM-5.3
- Darwin-36B-Opus vs WeVisOne-VL-4B
- DeepSeek-V3.2 vs GLM-5.3
- DeepSeek-V3.2 vs WeVisOne-VL-4B
- DeepSeek V4.1 Flash vs GLM-5.3
- DeepSeek V4.1 Flash vs WeVisOne-VL-4B
- DeepSeek-V4-Flash-0731 vs GLM-5.3
- DeepSeek-V4-Flash-0731 vs WeVisOne-VL-4B
- Devstral Small 2 vs GLM-5.3
- Devstral Small 2 vs WeVisOne-VL-4B
- DiffusionGemma 26B A4B IT vs GLM-5.3
- DiffusionGemma 26B A4B IT vs WeVisOne-VL-4B
- EXAONE 4.5 33B vs GLM-5.3
- EXAONE 4.5 33B vs WeVisOne-VL-4B
- Gemma 4 12B IT vs GLM-5.3
- Gemma 4 12B IT vs WeVisOne-VL-4B
- Gemma 4 26B A4B IT vs GLM-5.3
- Gemma 4 26B A4B IT vs WeVisOne-VL-4B
- Gemma 4 31B IT vs GLM-5.3
- Gemma 4 31B IT vs WeVisOne-VL-4B
- Gemma 4 E2B IT vs GLM-5.3
- Gemma 4 E2B IT vs WeVisOne-VL-4B
- Gemma 4 E4B IT vs GLM-5.3
- Gemma 4 E4B IT vs WeVisOne-VL-4B
- GLM-4.5-Air vs GLM-5.3
- GLM-4.5-Air vs WeVisOne-VL-4B
- GLM-4.7-Flash vs GLM-5.3
- GLM-4.7-Flash vs WeVisOne-VL-4B
- GLM-5.3-Flash vs WeVisOne-VL-4B
- 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 LongCat-2.5
- GLM-5.3 vs MiMo-V2.6 Distill Qwen 9B
- GLM-5.3 vs MiMo-V2.6-Flash-RL
- GLM-5.3 vs MiniCPM-o 4.5
- GLM-5.3 vs MiniCPM-V 4.7 1B
- 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
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- GLM-5.3 vs Mistral Large 4
- GLM-5.3 vs Muse Glimmer 30B
- GLM-5.3 vs Nanbeige4.2-3B
- 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 North Mini Code 1.0
- 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 OTel-2.0-LLM-31B-IT
- 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
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- GLM-5.3 vs Qwen3-VL-30B-A3B-Instruct
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- 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 SenseNova-Loop-9B-Preview
- GLM-5.3 vs SmolLM3-3B
- GLM-5.3 vs Spark-X2.6-4B
- GLM-5.3 vs Step 3.7 Flash
- 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
- GLM-5.3 vs WeVisOne-VL-2B
- GLM-5.3 vs ZAYA1-8B
- gpt-oss-120b vs WeVisOne-VL-4B
- gpt-oss-20b vs WeVisOne-VL-4B
- Granite 4.2 30B vs WeVisOne-VL-4B
- Granite 4.2 3B vs WeVisOne-VL-4B
- Granite 4.2 8B vs WeVisOne-VL-4B
- Hy-Memory-30B-A3B vs WeVisOne-VL-4B
- Hy3 vs WeVisOne-VL-4B
- Inkling-Small vs WeVisOne-VL-4B
- Inkling vs WeVisOne-VL-4B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs WeVisOne-VL-4B
- Kimi K3.1 Pro vs WeVisOne-VL-4B
- Kimi K3 vs WeVisOne-VL-4B
- Kolibri-1 vs WeVisOne-VL-4B
- Kronos vs WeVisOne-VL-4B
- Laguna M.1 vs WeVisOne-VL-4B
- Laguna S 2.1 vs WeVisOne-VL-4B
- Laguna XS 2.1 vs WeVisOne-VL-4B
- LBS-27B vs WeVisOne-VL-4B
- LFM2.5-2.6B vs WeVisOne-VL-4B
- LFM3-1.7B vs WeVisOne-VL-4B
- LFM3-Audio-2B vs WeVisOne-VL-4B
- LFM3-Omni-2B vs WeVisOne-VL-4B
- LFM3-VL-2B vs WeVisOne-VL-4B
- Ling-3.0-tiny vs WeVisOne-VL-4B
- Ling-3.1-flash-1019 vs WeVisOne-VL-4B
- LongCat-2.5 vs WeVisOne-VL-4B
- MiMo-V2.6 Distill Qwen 9B vs WeVisOne-VL-4B
- MiMo-V2.6-Flash-RL vs WeVisOne-VL-4B
- MiniCPM-o 4.5 vs WeVisOne-VL-4B
- MiniCPM-V 4.7 1B vs WeVisOne-VL-4B
- MiniCPM-V 4.7 35B-A3B vs WeVisOne-VL-4B
- MiniCPM5-2B vs WeVisOne-VL-4B
- MiniMax-M2.5 vs WeVisOne-VL-4B
- MiniMax-M2.7 vs WeVisOne-VL-4B
- MiniMax-M3.1-Flash vs WeVisOne-VL-4B
- Mistral Large 4 vs WeVisOne-VL-4B
- Muse Glimmer 30B vs WeVisOne-VL-4B
- Nanbeige4.2-3B vs WeVisOne-VL-4B
- Nanbeige4.3-33B vs WeVisOne-VL-4B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs WeVisOne-VL-4B
- Nemotron 3.5 Lightning 30B A3B vs WeVisOne-VL-4B
- Nemotron 3 Nano 30B A3B vs WeVisOne-VL-4B
- Nemotron 3 Nano 4B vs WeVisOne-VL-4B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs WeVisOne-VL-4B
- Nemotron 3 Super 120B A12B vs WeVisOne-VL-4B
- Nemotron 3 Ultra 550B A55B vs WeVisOne-VL-4B
- North Mini Code 1.0 vs WeVisOne-VL-4B
- occamy-1.0 vs WeVisOne-VL-4B
- Olmo 3 7B Think vs WeVisOne-VL-4B
- Ornith-1.5-122B vs WeVisOne-VL-4B
- Ornith-1.5-35B-A3B vs WeVisOne-VL-4B
- Ornith-1.5-397B vs WeVisOne-VL-4B
- Ornith-1.5-9B vs WeVisOne-VL-4B
- OTel-2.0-LLM-31B-IT vs WeVisOne-VL-4B
- Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-4B
- Qwen3-30B-A3B-Instruct-2507 vs WeVisOne-VL-4B
- Qwen3-4B-Instruct-2507 vs WeVisOne-VL-4B
- Qwen3.5-0.8B vs WeVisOne-VL-4B
- Qwen3.5-122B-A10B vs WeVisOne-VL-4B
- Qwen3.5-2B vs WeVisOne-VL-4B
- Qwen3.5-397B-A17B vs WeVisOne-VL-4B
- Qwen3.5-4B vs WeVisOne-VL-4B
- Qwen3.5-9B vs WeVisOne-VL-4B
- Qwen3.6-27B vs WeVisOne-VL-4B
- Qwen3.6-35B-A3B vs WeVisOne-VL-4B
- Qwen3.8-27B vs WeVisOne-VL-4B
- Qwen3.8-Flash-Next vs WeVisOne-VL-4B
- Qwen3-Coder-30B-A3B-Instruct vs WeVisOne-VL-4B
- Qwen3-Coder-Next vs WeVisOne-VL-4B
- Qwen3-Omni-30B-A3B-Instruct vs WeVisOne-VL-4B
- Qwen3-VL-2B-Instruct vs WeVisOne-VL-4B
- Qwen3-VL-30B-A3B-Instruct vs WeVisOne-VL-4B
- Qwen3-VL-32B-Instruct vs WeVisOne-VL-4B
- Qwen3-VL-4B-Instruct vs WeVisOne-VL-4B
- Qwen3-VL-8B-Instruct vs WeVisOne-VL-4B
- Qwopus3.8-27B-Flash-V2 vs WeVisOne-VL-4B
- Recursive Flint vs WeVisOne-VL-4B
- Ripple-65B Preview vs WeVisOne-VL-4B
- SenseNova-Loop-9B-Preview vs WeVisOne-VL-4B
- SmolLM3-3B vs WeVisOne-VL-4B
- Spark-X2.6-4B vs WeVisOne-VL-4B
- Step 3.7 Flash vs WeVisOne-VL-4B
- Swift 1.5 Qwen3.8-27B vs WeVisOne-VL-4B
- Swift 1.5 Qwen3.8-Flash-Next vs WeVisOne-VL-4B
- ThinkingCap (Qwen3.8 27B) vs WeVisOne-VL-4B
- WeVisOne-VL-2B vs WeVisOne-VL-4B
- WeVisOne-VL-4B vs ZAYA1-8B
- Baidu Unlimited-OCR-2 vs WeVisOne-VL-4B
Machine-readable
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anchor compare glm-5-3 wevisone-vl-4b(the CLI) - Over MCP
compare_tools {"a": "glm-5-3", "b": "wevisone-vl-4b"}at/mcp, no key