# GLM-4.5-Air vs GLM-5.3-Flash

> GLM-5.3-Flash scores 65.1 (B) 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-glm-5-3-flash
- Markdown: https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-5-3-flash.md (~5,950 tokens)
- Slim: https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-5-3-flash.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-5-3-flash.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-5.3-Flash scores 65.1 (B) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 2 of 5 scored categories. GLM-4.5-Air leads on reach, risk and agent interface. 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
- GLM-5.3-Flash: grade B, 65.1/100, rank #16 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-5-3-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.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:
- Reach, 65 against 60
- Risk, 65 against 55
- Agent interface, 85 against 70

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

### GLM-5.3-Flash (B)

Good for: Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider

Ahead on:
- Capability, 75 against 50
- Documentation and upkeep, 58 against 38

Watch for: Z.ai's smallest build is 328.4 GB in FP8, and no Ollama or LM Studio local build exists


## Score by category

| Category | Weight | GLM-4.5-Air | GLM-5.3-Flash | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 50 | 75 | GLM-5.3-Flash +25 |
| Reach | 25% (25 this run) | 65 | 60 | GLM-4.5-Air +5 |
| Risk | 20% (20 this run) | 65 | 55 | GLM-4.5-Air +10 |
| Agent interface | 10% (10 this run) | 85 | 70 | GLM-4.5-Air +15 |
| Documentation and upkeep | 10% (10 this run) | 38 | 58 | GLM-5.3-Flash +20 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **59.1 · C** | **65.1 · B** | |

## Facts side by side

| Fact | GLM-4.5-Air | GLM-5.3-Flash |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Z.ai (Zhipu AI) | Z.ai |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | MIT |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-08-11 | 2026-08-26 |
| 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.

**GLM-5.3-Flash.** An MIT-licensed multimodal agent and coding model with a 1M-token context and several tool calls per turn, served by vLLM, SGLang, llama.cpp and Transformers. Z.ai's smallest build is 328 GB in FP8, so it needs a multi-GPU server. The main risk is that nothing on safety or prompt injection is published and there is no way to report a flaw.

## 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

### GLM-5.3-Flash

1. Start it with `vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47` (vLLM 0.32.0 or later, about 386 GB of GPU memory per vLLM's recipe)
2. Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; calls come back as tool_call blocks with arg_key and arg_value pairs
3. Pass `chat_template_kwargs: {"reasoning_effort": "low"}` for short tasks; anything other than low or high falls back to max
4. Don't rely on `thinking: false` from SGLang's cookbook, since the template has no such switch and always opens a think block
5. Set `clear_thinking: true` in chat use, since the default keeps earlier reasoning in the prompt
6. On a 128 GB machine only third-party low-bit builds come near fitting, none of them from Z.ai

## Questions

### Which is better for AI agents, GLM-4.5-Air or GLM-5.3-Flash?

GLM-5.3-Flash scores 65.1 (B) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 2 of 5 scored categories. GLM-4.5-Air leads on reach, risk and agent interface.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-5-3-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-5-3-flash.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "glm-4-5-air", "b": "glm-5-3-flash"}`. From a terminal: `anchor compare glm-4-5-air glm-5-3-flash`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-4-5-air.json and https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.json

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