# GLM-5.3 vs Laguna M.1

> GLM-5.3 scores 64.6 (B) to Laguna M.1's 55.5 (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-5-3-vs-laguna-m-1
- Markdown: https://www.anchorterminal.com/compare/glm-5-3-vs-laguna-m-1.md (~5,750 tokens)
- Slim: https://www.anchorterminal.com/compare/glm-5-3-vs-laguna-m-1.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/glm-5-3-vs-laguna-m-1.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 Laguna M.1's 55.5 (C), and leads in 2 of 5 scored categories. Laguna M.1 leads on reach, risk and agent interface. Both do open-weight models you run yourself.

- 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
- Laguna M.1: grade C, 55.5/100, rank #79 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-m-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-m-1.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-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 46
- Documentation and upkeep, 58 against 36

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

### Laguna M.1 (C)

Good for: Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0.

Ahead on:
- Reach, 65 against 55
- Risk, 55 against 50
- Agent interface, 85 against 70

Watch for: The smallest build needs about 167 GB, more than a 128 GB machine


## Score by category

| Category | Weight | GLM-5.3 | Laguna M.1 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 80 | 46 | GLM-5.3 +34 |
| Reach | 25% (25 this run) | 55 | 65 | Laguna M.1 +10 |
| Risk | 20% (20 this run) | 50 | 55 | Laguna M.1 +5 |
| Agent interface | 10% (10 this run) | 70 | 85 | Laguna M.1 +15 |
| Documentation and upkeep | 10% (10 this run) | 58 | 36 | GLM-5.3 +22 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **64.6 · B** | **55.5 · C** | |

## Facts side by side

| Fact | GLM-5.3 | Laguna M.1 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Z.ai (Zhipu AI) | Poolside |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | GLM-5.3 License (MIT-style, with a security review for the largest model-as-a-service operators) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-14 | 2026-07-14 |
| Terms last updated | no document linked |  |
| Privacy policy last updated | 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) | 2.5/5 (4) |

## Verdicts

**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.

**Laguna M.1.** A large coding and agent cousin under plain Apache 2.0, for agents with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine, and no local runtime library packages it. The main risks are no upkeep since July, no hosted fallback shown today, no safety results, and repository code on the SGLang route.

## Before you call either

### 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

### Laguna M.1

1. Run the NVFP4 build on Blackwell GPUs with `vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on vLLM 0.21.0 or later
2. Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{"enable_thinking": true}'`, which Poolside recommends for agentic coding
3. Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped
4. Sampling per the card is temperature 1.0 and top_k 20
5. The FP8 build's Docker image and serve.sh predate upstream support and set a 4,096-token context, so prefer a released vLLM
6. It doesn't fit our 128 GB reference machine at any published precision
7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented

## Questions

### Which is better for AI agents, GLM-5.3 or Laguna M.1?

GLM-5.3 scores 64.6 (B) on agent readiness against Laguna M.1's 55.5 (C), and leads in 2 of 5 scored categories. Laguna M.1 leads on reach, risk and agent interface.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/glm-5-3-vs-laguna-m-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-5-3-vs-laguna-m-1.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "glm-5-3", "b": "laguna-m-1"}`. From a terminal: `anchor compare glm-5-3 laguna-m-1`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-5-3.json and https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json

## Other comparisons with GLM-5.3 or Laguna M.1

- [Agents-A1-4B vs GLM-5.3](https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3.md)
- [Agents-A1-4B vs Laguna M.1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-m-1.md)
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- [Laguna M.1 vs MiniCPM5-2B](https://www.anchorterminal.com/compare/laguna-m-1-vs-minicpm5-2b.md)
- [Laguna M.1 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-5.md)
- [Laguna M.1 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-7.md)
- [Laguna M.1 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/laguna-m-1-vs-muse-glimmer-30b.md)
- [Laguna M.1 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-5-lightning-30b-a3b.md)
- [Laguna M.1 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-nano-30b-a3b.md)
- [Laguna M.1 vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-nano-4b.md)
- [Laguna M.1 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)
- [Laguna M.1 vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-super-120b-a12b.md)
- [Laguna M.1 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-ultra-550b-a55b.md)
- [Laguna M.1 vs occamy-1.0](https://www.anchorterminal.com/compare/laguna-m-1-vs-occamy-1-0.md)
- [Laguna M.1 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/laguna-m-1-vs-olmo-3-7b-think.md)
- [Laguna M.1 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-35b-a3b.md)
- [Laguna M.1 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-397b.md)
- [Laguna M.1 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-9b.md)
- [Laguna M.1 vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-30b-a3b-instruct-2507.md)
- [Laguna M.1 vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-4b-instruct-2507.md)
- [Laguna M.1 vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-0-8b.md)
- [Laguna M.1 vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-122b-a10b.md)
- [Laguna M.1 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-2b.md)
- [Laguna M.1 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-397b-a17b.md)
- [Laguna M.1 vs Qwen3.5-4B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-4b.md)
- [Laguna M.1 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-9b.md)
- [Laguna M.1 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-6-27b.md)
- [Laguna M.1 vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-6-35b-a3b.md)
- [Laguna M.1 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-8-27b.md)
- [Laguna M.1 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-8-flash-next.md)
- [Laguna M.1 vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-coder-30b-a3b-instruct.md)
- [Laguna M.1 vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-coder-next.md)
- [Laguna M.1 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-omni-30b-a3b-instruct.md)
- [Laguna M.1 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-2b-instruct.md)
- [Laguna M.1 vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-30b-a3b-instruct.md)
- [Laguna M.1 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-32b-instruct.md)
- [Laguna M.1 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-4b-instruct.md)
- [Laguna M.1 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-8b-instruct.md)
- [Laguna M.1 vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwopus3-8-27b-flash-v2.md)
- [Laguna M.1 vs SmolLM3-3B](https://www.anchorterminal.com/compare/laguna-m-1-vs-smollm3-3b.md)
- [Laguna M.1 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/laguna-m-1-vs-spark-x2-6-4b.md)
- [Laguna M.1 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-27b.md)
- [Laguna M.1 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-flash-next.md)
- [Laguna M.1 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/laguna-m-1-vs-thinkingcap-qwen3-8-27b.md)

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