# GLM-4.5-Air vs Qwen3-4B-Instruct-2507

> GLM-4.5-Air scores 59.1 (C) to Qwen3-4B-Instruct-2507's 55.7 (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-qwen3-4b-instruct-2507
- Markdown: https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-4b-instruct-2507.md (~6,300 tokens)
- Slim: https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-4b-instruct-2507.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-4b-instruct-2507.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

GLM-4.5-Air scores 59.1 (C) on agent readiness against Qwen3-4B-Instruct-2507's 55.7 (C), and leads in 4 of 5 scored categories. Qwen3-4B-Instruct-2507 leads on reach. 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
- Qwen3-4B-Instruct-2507: grade C, 55.7/100, rank #77 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-4b-instruct-2507.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:
- Capability, 50 against 31
- Risk, 65 against 55
- Agent interface, 85 against 75
- Documentation and upkeep, 38 against 26

Watch for: Z.ai's smallest build is FP8 at about 135 GB in use, with no 4-bit build of its own

### Qwen3-4B-Instruct-2507 (C)

Good for: Agents that want a capable small instruct model on a laptop, phone-class device or spare GPU, with a plain licence and long context.

Ahead on:
- Reach, 95 against 65

Watch for: No safety results, prompt-injection guidance or security contact


## Score by category

| Category | Weight | GLM-4.5-Air | Qwen3-4B-Instruct-2507 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 50 | 31 | GLM-4.5-Air +19 |
| Reach | 25% (25 this run) | 65 | 95 | Qwen3-4B-Instruct-2507 +30 |
| Risk | 20% (20 this run) | 65 | 55 | GLM-4.5-Air +10 |
| Agent interface | 10% (10 this run) | 85 | 75 | GLM-4.5-Air +10 |
| Documentation and upkeep | 10% (10 this run) | 38 | 26 | GLM-4.5-Air +12 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **59.1 · C** | **55.7 · C** | |

## Facts side by side

| Fact | GLM-4.5-Air | Qwen3-4B-Instruct-2507 |
| --- | --- | --- |
| 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 | 2025-09-17 |
| 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.

**Qwen3-4B-Instruct-2507.** A small, fast cousin for instruction following, short tool-calling jobs and long documents on modest hardware, with no thinking step. It runs in about 3 GB at 4 bits on every common runtime, and two providers host it. The main risk is neglect, with no file changes in over a year, no answers from Alibaba and no safety results.

## 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

### Qwen3-4B-Instruct-2507

1. Quickest start is `ollama run qwen3:4b-instruct`, 2.5 GB. Plain `qwen3:4b` is the Thinking-2507 edition, not this one
2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3-4B-Instruct-2507 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser hermes`
3. Use the hermes parser in vLLM or qwen25 in SGLang, the template emits JSON inside tool_call tags
4. Don't pass enable_thinking, this edition never thinks and the card says the flag isn't needed
5. Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and min_p 0, with presence_penalty up to 2 against repetition
6. Drop the context to 32,768 tokens if memory runs out, as the card suggests
7. Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented

## Questions

### Which is better for AI agents, GLM-4.5-Air or Qwen3-4B-Instruct-2507?

GLM-4.5-Air scores 59.1 (C) on agent readiness against Qwen3-4B-Instruct-2507's 55.7 (C), and leads in 4 of 5 scored categories. Qwen3-4B-Instruct-2507 leads on reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-4b-instruct-2507.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-4b-instruct-2507.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "glm-4-5-air", "b": "qwen3-4b-instruct-2507"}`. From a terminal: `anchor compare glm-4-5-air qwen3-4b-instruct-2507`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-4-5-air.json and https://www.anchorterminal.com/api/v1/tools/qwen3-4b-instruct-2507.json

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- [Ornith-1.5-35B-A3B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-4b-instruct-2507.md)
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- [Qwen3-4B-Instruct-2507 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-swift-1-5-qwen3-8-27b.md)
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