# GLM-4.5-Air vs Qwen3-VL-2B-Instruct > Qwen3-VL-2B-Instruct scores 61 (C) to GLM-4.5-Air's 59.1 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-vl-2b-instruct - Markdown: https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-vl-2b-instruct.md (~6,200 tokens) - Slim: https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-vl-2b-instruct.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-vl-2b-instruct.json (this page as data, same URL with Accept: application/json) - Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt) - API: https://www.anchorterminal.com/api/v1/index.json - MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-10-21 Qwen3-VL-2B-Instruct scores 61 (C) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 1 of 4 scored categories. GLM-4.5-Air leads on risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself. - GLM-4.5-Air: grade C, 59.1/100, rank #62 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-4-5-air.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-4-5-air.json - Qwen3-VL-2B-Instruct: grade C, 61/100, rank #45 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-vl-2b-instruct.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### GLM-4.5-Air (C) Good 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: - Risk, 65 against 55 - Agent interface, 85 against 60 - Documentation and upkeep, 38 against 31 Watch for: Z.ai's smallest build is FP8 at about 135 GB in use, with no 4-bit build of its own ### Qwen3-VL-2B-Instruct (C) Good for: An agent on a laptop or small edge device that needs a local model to read images, documents and video in about 2 GB Ahead on: - Reach, 95 against 65 Watch for: No safety evaluation, agent-use guidance or security contact ## Score by category | Category | Weight | GLM-4.5-Air | Qwen3-VL-2B-Instruct | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 65 | 95 | Qwen3-VL-2B-Instruct +30 | | Risk | 20% (20 this run) | 65 | 55 | GLM-4.5-Air +10 | | Agent interface | 10% (10 this run) | 85 | 60 | GLM-4.5-Air +25 | | Documentation and upkeep | 10% (10 this run) | 38 | 31 | GLM-4.5-Air +7 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **59.1 · C** | **61 · C** | | ## Facts side by side | Fact | GLM-4.5-Air | Qwen3-VL-2B-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) | 2.8/5 (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-2B-Instruct.** The smallest Qwen3-VL cousin, for reading screenshots, documents, charts and video on a laptop or edge box, running in about 2 GB from Alibaba's Q4_K_M GGUF in every major runtime under plain Apache 2.0. Qwen hasn't answered repository questions or published safety work, so keep a human or a check in the loop before it drives a GUI. ## Before you call either ### GLM-4.5-Air 1. 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 2. Use the glm45 tool and reasoning parsers for this model, the glm47 parser in Z.ai's current README is for GLM-4.7 3. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks, which makes the template add /nothink and an empty think block 4. Declare tools in OpenAI style, and the template renders arguments as arg_key and arg_value pairs that the parser turns back into JSON 5. Keep prompts under 128,000 tokens, the tokenizer config's limit, though config.json allows 131,072 6. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented for the weights ### Qwen3-VL-2B-Instruct 1. Start it with `llama-server -hf Qwen/Qwen3-VL-2B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:2b-instruct` 2. In Ollama the plain `qwen3-vl:2b` tag is the Thinking edition, so name `2b-instruct` 3. With the GGUF, load the mmproj vision file too, or images won't be read 4. Parse tool calls as JSON inside `` tags, or use SGLang's `--tool-call-parser qwen` 5. Use the card's sampling, `temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5` for vision and `temperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0` for text 6. Validate JSON output yourself, since structured output isn't documented 7. vLLM needs 0.11.0 or later and Transformers 4.57.0 or later ## Questions ### Which is better for AI agents, GLM-4.5-Air or Qwen3-VL-2B-Instruct? Qwen3-VL-2B-Instruct scores 61 (C) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 1 of 4 scored categories. GLM-4.5-Air leads on risk, agent interface and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-vl-2b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-vl-2b-instruct.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "glm-4-5-air", "b": "qwen3-vl-2b-instruct"}`. 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[MiniMax-M2.5 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-2b-instruct.md) - [MiniMax-M2.7 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-2b-instruct.md) - [Muse Glimmer 30B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-2b-instruct.md) - [Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-2b-instruct.md) - [Nemotron 3 Nano 30B A3B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-2b-instruct.md) - [Nemotron 3 Nano 4B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-2b-instruct.md) - [Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-vl-2b-instruct.md) - [Nemotron 3 Super 120B A12B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-vl-2b-instruct.md) - 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[Qwen3.8-Flash-Next vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-2b-instruct.md) - [Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-2b-instruct.md) - [Qwen3-Coder-Next vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-2b-instruct.md) - [Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-2b-instruct.md) - [Qwen3-VL-2B-Instruct vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwen3-vl-30b-a3b-instruct.md) - [Qwen3-VL-2B-Instruct vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwen3-vl-32b-instruct.md) - [Qwen3-VL-2B-Instruct vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwen3-vl-4b-instruct.md) - [Qwen3-VL-2B-Instruct vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwen3-vl-8b-instruct.md) - [Qwen3-VL-2B-Instruct vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwopus3-8-27b-flash-v2.md) - [Qwen3-VL-2B-Instruct vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-smollm3-3b.md) - [Qwen3-VL-2B-Instruct vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-spark-x2-6-4b.md) - [Qwen3-VL-2B-Instruct vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-swift-1-5-qwen3-8-27b.md) - [Qwen3-VL-2B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-swift-1-5-qwen3-8-flash-next.md) - [Qwen3-VL-2B-Instruct vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-thinkingcap-qwen3-8-27b.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp