Head to head · Open-weight models you run yourself · October 2026 research run
GLM-5.3 vs Qwen3.5-4B
GLM-5.3 scores 64.6 (B) on agent readiness against Qwen3.5-4B's 61.2 (C), and leads in 2 of 5 scored categories. Qwen3.5-4B 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 2870 open weights comparisons
GLM-5.3Z.ai (Zhipu AI) · Open-weight model
B64.6/100#17 of 100 open-weight models
Qwen3.5-4BAlibaba (Qwen team) · Open-weight model
C61.2/100#33 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
- Capability, 80 against 53
- Documentation and upkeep, 58 against 34
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 want a cheap local helper for tool calls, short reasoning and reading images on a laptop or small GPU
Ahead on
- Reach, 85 against 55
- Risk, 55 against 50
Watch for
No quantised build from Alibaba for this size
Score by category
| Category | Weight this run | GLM-5.3 | Qwen3.5-4B | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 80 | 53 | GLM-5.3 +27 |
| Reach | 25%25 | 55 | 85 | Qwen3.5-4B +30 |
| Risk | 20%20 | 50 | 55 | Qwen3.5-4B +5 |
| Agent interface | 10%10 | 70 | 70 | even |
| Documentation and upkeep | 10%10 | 58 | 34 | GLM-5.3 +24 |
| Negative events | ≤15 | 0 | 0 | |
| Total | B 64.6/100 | C 61.2/100 |
Facts side by side
| Fact | GLM-5.3 | Qwen3.5-4B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Z.ai (Zhipu AI) | Alibaba (Qwen team) |
| 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 | 2026-10-14 | 2026-03-02 |
| 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) | 3/5 (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.
Qwen3.5-4B
A small vision-language cousin for tool calls, light agent steps and image or video reading on almost any machine, with switchable thinking. Ollama's 3.3 GB build or the 9.3 GB BF16 files run in every major runtime under plain Apache 2.0. The main risks are no safety results, no structured-output guarantee and no answers from Alibaba since March.
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
Qwen3.5-4B
- Smallest start is
ollama run qwen3.5:4b(Q4_K_M, about 4 GB with context) - Full precision
vllm serve Qwen/Qwen3.5-4B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder - Turn thinking off with
"chat_template_kwargs": {"enable_thinking": false}in the request body, since it's on by default and/thinkand/nothinkaren't supported - There's no generation_config.json, so pass the card's sampling yourself, for example
temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5without thinking - Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens
- Validate any JSON it returns, since structured output isn't documented
Questions
Which is better for AI agents, GLM-5.3 or Qwen3.5-4B?
GLM-5.3 scores 64.6 (B) on agent readiness against Qwen3.5-4B's 61.2 (C), and leads in 2 of 5 scored categories. Qwen3.5-4B leads on reach and risk.
Other comparisons with GLM-5.3 or Qwen3.5-4B
- Agents-A1-4B vs GLM-5.3
- Agents-A1-4B vs Qwen3.5-4B
- Apertus v1.5 8B vs GLM-5.3
- Apertus v1.5 8B vs Qwen3.5-4B
- Apodex-1.1-mini vs GLM-5.3
- Apodex-1.1-mini vs Qwen3.5-4B
- BigBang-v1 vs GLM-5.3
- BigBang-v1 vs Qwen3.5-4B
- Cosmos-Reason2-2B vs GLM-5.3
- Cosmos-Reason2-2B vs Qwen3.5-4B
- Darwin-36B-Opus vs GLM-5.3
- Darwin-36B-Opus vs Qwen3.5-4B
- DeepSeek-V3.2 vs GLM-5.3
- DeepSeek-V3.2 vs Qwen3.5-4B
- DeepSeek V4.1 Flash vs GLM-5.3
- DeepSeek V4.1 Flash vs Qwen3.5-4B
- DeepSeek-V4-Flash-0731 vs GLM-5.3
- DeepSeek-V4-Flash-0731 vs Qwen3.5-4B
- DiffusionGemma 26B A4B IT vs GLM-5.3
- DiffusionGemma 26B A4B IT vs Qwen3.5-4B
- Gemma 4 12B IT vs GLM-5.3
- Gemma 4 12B IT vs Qwen3.5-4B
- Gemma 4 26B A4B IT vs GLM-5.3
- Gemma 4 26B A4B IT vs Qwen3.5-4B
- Gemma 4 31B IT vs GLM-5.3
- Gemma 4 31B IT vs Qwen3.5-4B
- Gemma 4 E2B IT vs GLM-5.3
- Gemma 4 E2B IT vs Qwen3.5-4B
- Gemma 4 E4B IT vs GLM-5.3
- Gemma 4 E4B IT vs Qwen3.5-4B
- GLM-4.5-Air vs GLM-5.3
- GLM-4.5-Air vs Qwen3.5-4B
- GLM-4.7-Flash vs GLM-5.3
- GLM-4.7-Flash vs Qwen3.5-4B
- GLM-5.3-Flash vs Qwen3.5-4B
- 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 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 MiniCPM-o 4.5
- 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 Qwen3.5-9B
- 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 Qwen3.5-4B
- gpt-oss-20b vs Qwen3.5-4B
- Granite 4.2 3B vs Qwen3.5-4B
- Hy3 vs Qwen3.5-4B
- Inkling-Small vs Qwen3.5-4B
- Inkling vs Qwen3.5-4B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.5-4B
- Kimi K3 vs Qwen3.5-4B
- Kolibri-1 vs Qwen3.5-4B
- Laguna M.1 vs Qwen3.5-4B
- Laguna S 2.1 vs Qwen3.5-4B
- Laguna XS 2.1 vs Qwen3.5-4B
- LFM2.5-2.6B vs Qwen3.5-4B
- Ling-3.0-tiny vs Qwen3.5-4B
- MiMo-V2.6 Distill Qwen 9B vs Qwen3.5-4B
- MiniCPM-o 4.5 vs Qwen3.5-4B
- MiniCPM5-2B vs Qwen3.5-4B
- MiniMax-M2.5 vs Qwen3.5-4B
- MiniMax-M2.7 vs Qwen3.5-4B
- Muse Glimmer 30B vs Qwen3.5-4B
- Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-4B
- Nemotron 3 Nano 30B A3B vs Qwen3.5-4B
- Nemotron 3 Nano 4B vs Qwen3.5-4B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3.5-4B
- Nemotron 3 Super 120B A12B vs Qwen3.5-4B
- Nemotron 3 Ultra 550B A55B vs Qwen3.5-4B
- occamy-1.0 vs Qwen3.5-4B
- Olmo 3 7B Think vs Qwen3.5-4B
- Ornith-1.5-35B-A3B vs Qwen3.5-4B
- Ornith-1.5-397B vs Qwen3.5-4B
- Ornith-1.5-9B vs Qwen3.5-4B
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3.5-4B
- Qwen3-4B-Instruct-2507 vs Qwen3.5-4B
- Qwen3.5-0.8B vs Qwen3.5-4B
- Qwen3.5-122B-A10B vs Qwen3.5-4B
- Qwen3.5-2B vs Qwen3.5-4B
- Qwen3.5-397B-A17B vs Qwen3.5-4B
- Qwen3.5-4B vs Qwen3.5-9B
- Qwen3.5-4B vs Qwen3.6-27B
- Qwen3.5-4B vs Qwen3.6-35B-A3B
- Qwen3.5-4B vs Qwen3.8-27B
- Qwen3.5-4B vs Qwen3.8-Flash-Next
- Qwen3.5-4B vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3.5-4B vs Qwen3-Coder-Next
- Qwen3.5-4B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3.5-4B vs Qwen3-VL-2B-Instruct
- Qwen3.5-4B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.5-4B vs Qwen3-VL-32B-Instruct
- Qwen3.5-4B vs Qwen3-VL-4B-Instruct
- Qwen3.5-4B vs Qwen3-VL-8B-Instruct
- Qwen3.5-4B vs Qwopus3.8-27B-Flash-V2
- Qwen3.5-4B vs SmolLM3-3B
- Qwen3.5-4B vs Spark-X2.6-4B
- Qwen3.5-4B vs Swift 1.5 Qwen3.8-27B
- Qwen3.5-4B vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3.5-4B vs ThinkingCap (Qwen3.8 27B)
Machine-readable
- This page as Markdown
/compare/glm-5-3-vs-qwen3-5-4b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/glm-5-3.json·/api/v1/tools/qwen3-5-4b.json - From a terminal
anchor compare glm-5-3 qwen3-5-4b(the CLI) - Over MCP
compare_tools {"a": "glm-5-3", "b": "qwen3-5-4b"}at/mcp, no key