# GLM-4.7-Flash vs GLM-5.3

> GLM-5.3 scores 64.6 (B) to GLM-4.7-Flash's 63 (B) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-glm-5-3
- Markdown: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-glm-5-3.md (~5,850 tokens)
- Slim: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-glm-5-3.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-glm-5-3.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 scores 64.6 (B) on agent readiness against GLM-4.7-Flash's 63 (B), and leads in 2 of 5 scored categories. GLM-4.7-Flash leads on reach, risk and agent interface. Both do open-weight models you run yourself.

- GLM-4.7-Flash: grade B, 63/100, rank #26 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-4-7-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.json
- GLM-5.3: grade B, 64.6/100, rank #17 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-5-3.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-5-3.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-4.7-Flash (B)

Good for: Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime

Ahead on:
- Reach, 80 against 55
- Risk, 55 against 50
- Agent interface, 85 against 70

Watch for: No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January

### GLM-5.3 (B)

Good 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 59
- Documentation and upkeep, 58 against 28

Watch for: Z.ai's smallest build is 755.6 GB in FP8, with no local Ollama or LM Studio build


## Score by category

| Category | Weight | GLM-4.7-Flash | GLM-5.3 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 59 | 80 | GLM-5.3 +21 |
| Reach | 25% (25 this run) | 80 | 55 | GLM-4.7-Flash +25 |
| Risk | 20% (20 this run) | 55 | 50 | GLM-4.7-Flash +5 |
| Agent interface | 10% (10 this run) | 85 | 70 | GLM-4.7-Flash +15 |
| Documentation and upkeep | 10% (10 this run) | 28 | 58 | GLM-5.3 +30 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **63 · B** | **64.6 · B** | |

## Facts side by side

| Fact | GLM-4.7-Flash | GLM-5.3 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Z.ai (Zhipu AI) | Z.ai (Zhipu AI) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | GLM-5.3 License (MIT-style, with a security review for the largest model-as-a-service operators) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-01-29 | 2026-10-14 |
| 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-4.7-Flash.** A small MIT-licensed mixture-of-experts model with about 3B active parameters, for local agent and coding work with several tool calls per turn and optional thinking. It runs in Ollama at about 23 GB and in nearly every common runtime. The main risk is neglect, with no changes or replies since early February and no safety results.

**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.

## Before you call either

### GLM-4.7-Flash

1. On our reference machine start it with `vllm serve zai-org/GLM-4.7-Flash --max-model-len 65536 --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-flash`
2. Or run `ollama run glm-4.7-flash` for the 19 GB q4_K_M build
3. Without the glm47 and glm45 parsers, tool calls and reasoning come back as plain text
4. Use a released vLLM (0.32.0 or later) or SGLang (0.5.22 or later); the card's nightly and pull-request builds date from January 2026
5. Pass `chat_template_kwargs: {"enable_thinking": false}` for short tasks, since thinking is on by default
6. Set `clear_thinking: false` to keep earlier reasoning in multi-turn agent work, as Z.ai does for τ²-Bench and Terminal Bench
7. Set the context length yourself, since config.json allows 202,752 tokens and tokenizer_config.json says 128,000

### GLM-5.3

1. 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)
2. Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; SGLang's cookbook says glm45 leaves the calls as plain text
3. Pass `chat_template_kwargs: {"reasoning_effort": "low"}` for short tasks; anything other than low or high falls back to max
4. Pass `clear_thinking: true` in chat use, since the template keeps earlier reasoning by default
5. Don't send `enable_thinking: false`; the template ignores it and always opens a think block
6. Validate JSON output yourself, since structured output is documented only for Z.ai's hosted API
7. No Z.ai build fits a 128 GB machine; the full 1M context needs 8xB200 per vLLM's recipe

## Questions

### Which is better for AI agents, GLM-4.7-Flash or GLM-5.3?

GLM-5.3 scores 64.6 (B) on agent readiness against GLM-4.7-Flash's 63 (B), and leads in 2 of 5 scored categories. GLM-4.7-Flash leads on reach, risk and agent interface.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-glm-5-3.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-glm-5-3.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "glm-4-7-flash", "b": "glm-5-3"}`. From a terminal: `anchor compare glm-4-7-flash glm-5-3`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.json and https://www.anchorterminal.com/api/v1/tools/glm-5-3.json

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- [GLM-5.3 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/glm-5-3-vs-laguna-xs-2-1.md)
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- [GLM-5.3 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/glm-5-3-vs-minimax-m2-5.md)
- [GLM-5.3 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/glm-5-3-vs-minimax-m2-7.md)
- [GLM-5.3 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/glm-5-3-vs-muse-glimmer-30b.md)
- [GLM-5.3 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-5-lightning-30b-a3b.md)
- [GLM-5.3 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-nano-30b-a3b.md)
- [GLM-5.3 vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-nano-4b.md)
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- [GLM-5.3 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-ultra-550b-a55b.md)
- [GLM-5.3 vs occamy-1.0](https://www.anchorterminal.com/compare/glm-5-3-vs-occamy-1-0.md)
- [GLM-5.3 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/glm-5-3-vs-olmo-3-7b-think.md)
- [GLM-5.3 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-35b-a3b.md)
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- [GLM-5.3 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-5-397b-a17b.md)
- [GLM-5.3 vs Qwen3.5-4B](https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-5-4b.md)
- [GLM-5.3 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-5-9b.md)
- [GLM-5.3 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-6-27b.md)
- [GLM-5.3 vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-6-35b-a3b.md)
- [GLM-5.3 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-8-27b.md)
- [GLM-5.3 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-8-flash-next.md)
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- [GLM-5.3 vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-coder-next.md)
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- [GLM-5.3 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-vl-2b-instruct.md)
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- [GLM-5.3 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-vl-32b-instruct.md)
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- [GLM-5.3 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-vl-8b-instruct.md)
- [GLM-5.3 vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/glm-5-3-vs-qwopus3-8-27b-flash-v2.md)
- [GLM-5.3 vs SmolLM3-3B](https://www.anchorterminal.com/compare/glm-5-3-vs-smollm3-3b.md)
- [GLM-5.3 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/glm-5-3-vs-spark-x2-6-4b.md)
- [GLM-5.3 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/glm-5-3-vs-swift-1-5-qwen3-8-27b.md)
- [GLM-5.3 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/glm-5-3-vs-swift-1-5-qwen3-8-flash-next.md)
- [GLM-5.3 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/glm-5-3-vs-thinkingcap-qwen3-8-27b.md)

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