Head to head · Open-weight models you run yourself · October 2026 research run
GLM-4.5-Air vs Qwen3-VL-30B-A3B-Instruct
GLM-4.5-Air scores 59.1 (C) on agent readiness against Qwen3-VL-30B-A3B-Instruct's 58 (C), and leads in 4 of 5 scored categories. Qwen3-VL-30B-A3B-Instruct leads on reach. 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-4.5-AirZ.ai (Zhipu AI) · Open-weight model
C59.1/100#62 of 100 open-weight models
Qwen3-VL-30B-A3B-InstructAlibaba (Qwen team) · Open-weight model
C58/100#68 of 100 open-weight models
Which one, for what
Best 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
- Capability, 50 against 39
- Risk, 65 against 55
- Agent interface, 85 against 60
Watch for
Z.ai's smallest build is FP8 at about 135 GB in use, with no 4-bit build of its own
Best for An agent with 24 GB or more to spare that wants a faster, stronger local reader of images, documents and video than the 8B model
Ahead on
- Reach, 95 against 65
Watch for
No safety evaluation, agent-use guidance or security contact
Score by category
| Category | Weight this run | GLM-4.5-Air | Qwen3-VL-30B-A3B-Instruct | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 50 | 39 | GLM-4.5-Air +11 |
| Reach | 25%25 | 65 | 95 | Qwen3-VL-30B-A3B-Instruct +30 |
| Risk | 20%20 | 65 | 55 | GLM-4.5-Air +10 |
| Agent interface | 10%10 | 85 | 60 | GLM-4.5-Air +25 |
| Documentation and upkeep | 10%10 | 38 | 36 | GLM-4.5-Air +2 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 59.1/100 | C 58/100 |
Facts side by side
| Fact | GLM-4.5-Air | Qwen3-VL-30B-A3B-Instruct |
|---|---|---|
| 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 | MIT | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-08-11 | 2025-11-01 |
| 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-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.
Qwen3-VL-30B-A3B-Instruct
A mid-sized vision-language cousin for screenshots, documents, charts and video that runs fast for its size, with about 3B active parameters. It needs about 23 GB for Alibaba's Q4_K_M GGUF with its vision file, loads in every major runtime under plain Apache 2.0, and three providers host it. With no published safety work, add guardrails before it drives a GUI.
Before you call either
GLM-4.5-Air
- 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 - Use the glm45 tool and reasoning parsers for this model, the glm47 parser in Z.ai's current README is for GLM-4.7
- Thinking is on by default, pass
chat_template_kwargswithenable_thinkingfalse for short tasks, which makes the template add /nothink and an empty think block - Declare tools in OpenAI style, and the template renders arguments as arg_key and arg_value pairs that the parser turns back into JSON
- Keep prompts under 128,000 tokens, the tokenizer config's limit, though config.json allows 131,072
- Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented for the weights
Qwen3-VL-30B-A3B-Instruct
- Start it with
llama-server -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M --jinja -c 32768, orollama run qwen3-vl:30b-a3b-instruct - In Ollama the plain
qwen3-vl:30btag is the Thinking edition, so name30b-a3b-instruct - With the GGUF, load the mmproj vision file too, or images won't be read
- Parse tool calls as JSON inside
<tool_call>tags, or use SGLang's--tool-call-parser qwen - Use the shipped sampling, temperature 0.7, top_p 0.8 and top_k 20, from generation_config.json
- Validate JSON output yourself, since structured output isn't documented
- vLLM needs 0.11.0 or later, Transformers 4.57.0 or later and llama.cpp b6907 or later
Questions
Which is better for AI agents, GLM-4.5-Air or Qwen3-VL-30B-A3B-Instruct?
GLM-4.5-Air scores 59.1 (C) on agent readiness against Qwen3-VL-30B-A3B-Instruct's 58 (C), and leads in 4 of 5 scored categories. Qwen3-VL-30B-A3B-Instruct leads on reach.
Other comparisons with GLM-4.5-Air or Qwen3-VL-30B-A3B-Instruct
- Agents-A1-4B vs GLM-4.5-Air
- Agents-A1-4B vs Qwen3-VL-30B-A3B-Instruct
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- Darwin-36B-Opus vs GLM-4.5-Air
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- DeepSeek-V3.2 vs GLM-4.5-Air
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- DeepSeek-V4-Flash-0731 vs GLM-4.5-Air
- DeepSeek-V4-Flash-0731 vs Qwen3-VL-30B-A3B-Instruct
- DiffusionGemma 26B A4B IT vs GLM-4.5-Air
- DiffusionGemma 26B A4B IT vs Qwen3-VL-30B-A3B-Instruct
- Gemma 4 12B IT vs GLM-4.5-Air
- Gemma 4 12B IT vs Qwen3-VL-30B-A3B-Instruct
- Gemma 4 26B A4B IT vs GLM-4.5-Air
- Gemma 4 26B A4B IT vs Qwen3-VL-30B-A3B-Instruct
- Gemma 4 31B IT vs GLM-4.5-Air
- Gemma 4 31B IT vs Qwen3-VL-30B-A3B-Instruct
- Gemma 4 E2B IT vs GLM-4.5-Air
- Gemma 4 E2B IT vs Qwen3-VL-30B-A3B-Instruct
- Gemma 4 E4B IT vs GLM-4.5-Air
- Gemma 4 E4B IT vs Qwen3-VL-30B-A3B-Instruct
- GLM-4.5-Air vs GLM-4.7-Flash
- GLM-4.5-Air vs GLM-5.3
- GLM-4.5-Air vs GLM-5.3-Flash
- GLM-4.5-Air vs gpt-oss-120b
- GLM-4.5-Air vs gpt-oss-20b
- GLM-4.5-Air vs Granite 4.2 3B
- GLM-4.5-Air vs Hy3
- GLM-4.5-Air vs Inkling
- GLM-4.5-Air vs Inkling-Small
- GLM-4.5-Air vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- GLM-4.5-Air vs Kimi K3
- GLM-4.5-Air vs Kolibri-1
- GLM-4.5-Air vs Laguna M.1
- GLM-4.5-Air vs Laguna S 2.1
- GLM-4.5-Air vs Laguna XS 2.1
- GLM-4.5-Air vs LFM2.5-2.6B
- GLM-4.5-Air vs Ling-3.0-tiny
- GLM-4.5-Air vs MiMo-V2.6 Distill Qwen 9B
- GLM-4.5-Air vs MiniCPM-o 4.5
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- GLM-5.3 vs Qwen3-VL-30B-A3B-Instruct
- gpt-oss-120b vs Qwen3-VL-30B-A3B-Instruct
- gpt-oss-20b vs Qwen3-VL-30B-A3B-Instruct
- Granite 4.2 3B vs Qwen3-VL-30B-A3B-Instruct
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- Qwen3.5-4B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.5-9B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.6-27B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.6-35B-A3B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.8-27B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.8-Flash-Next vs Qwen3-VL-30B-A3B-Instruct
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- Qwen3-Coder-Next vs Qwen3-VL-30B-A3B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-30B-A3B-Instruct
- Qwen3-VL-2B-Instruct vs Qwen3-VL-30B-A3B-Instruct
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- Qwen3-VL-30B-A3B-Instruct vs Qwen3-VL-8B-Instruct
- Qwen3-VL-30B-A3B-Instruct vs Qwopus3.8-27B-Flash-V2
- Qwen3-VL-30B-A3B-Instruct vs SmolLM3-3B
- Qwen3-VL-30B-A3B-Instruct vs Spark-X2.6-4B
- Qwen3-VL-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-VL-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-VL-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)
Machine-readable
- This page as Markdown
/compare/glm-4-5-air-vs-qwen3-vl-30b-a3b-instruct.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/glm-4-5-air.json·/api/v1/tools/qwen3-vl-30b-a3b-instruct.json - From a terminal
anchor compare glm-4-5-air qwen3-vl-30b-a3b-instruct(the CLI) - Over MCP
compare_tools {"a": "glm-4-5-air", "b": "qwen3-vl-30b-a3b-instruct"}at/mcp, no key