# GLM-4.5-Air vs Nemotron 3 Nano 30B A3B

> Nemotron 3 Nano 30B A3B scores 67.1 (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-nemotron-3-nano-30b-a3b
- Markdown: https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-nano-30b-a3b.md (~6,550 tokens)
- Slim: https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-nano-30b-a3b.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-nano-30b-a3b.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

Nemotron 3 Nano 30B A3B scores 67.1 (B) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 3 of 5 scored categories. GLM-4.5-Air leads on risk. 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
- Nemotron 3 Nano 30B A3B: grade B, 67.1/100, rank #12 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.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 60

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

### Nemotron 3 Nano 30B A3B (B)

Good for: Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.

Ahead on:
- Reach, 90 against 65
- Agent interface, 90 against 85
- Documentation and upkeep, 75 against 38

Also in its favour:
- Rated higher by the reviewer agents, 3.3 against 2.8 out of 5

Watch for: The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository


## Score by category

| Category | Weight | GLM-4.5-Air | Nemotron 3 Nano 30B A3B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 50 | 46 | GLM-4.5-Air +4 |
| Reach | 25% (25 this run) | 65 | 90 | Nemotron 3 Nano 30B A3B +25 |
| Risk | 20% (20 this run) | 65 | 60 | GLM-4.5-Air +5 |
| Agent interface | 10% (10 this run) | 85 | 90 | Nemotron 3 Nano 30B A3B +5 |
| Documentation and upkeep | 10% (10 this run) | 38 | 75 | Nemotron 3 Nano 30B A3B +37 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **59.1 · C** | **67.1 · B** | |

## Facts side by side

| Fact | GLM-4.5-Air | Nemotron 3 Nano 30B A3B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Z.ai (Zhipu AI) | NVIDIA |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-08-11 | 2026-08-24 |
| Terms last updated | 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) | 3.3/5 (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.

**Nemotron 3 Nano 30B A3B.** A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March.

## 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

### Nemotron 3 Nano 30B A3B

1. Start it with `ollama run nemotron-3-nano:30b` (18 GB MLX on a Mac, 24 GB q4_K_M elsewhere)
2. On the reference machine use BF16 with vLLM, after `wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py`, then `vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`
3. NVFP4 needs an NVIDIA Blackwell GPU, and FP8 isn't documented on gfx1151
4. Without the qwen3_coder tool parser and the nano_v3 reasoning parser, tool calls and reasoning come back as plain text
5. Card sampling is temperature 0.6 and top_p 0.95 for tool calling, temperature 1.0 and top_p 1.0 for reasoning
6. Thinking is on by default, so pass enable_thinking as false in chat_template_kwargs for short tasks
7. Set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 and raise --max-model-len only when you need more than 262,144 tokens
8. In SGLang the main branch names the reasoning parser nemotron_3, not the card's nano_v3
9. Filter untrusted tool results before they reach the model, as NVIDIA warns prompt injection can lead to code execution
10. Validate JSON output yourself or use the runtime's guided decoding, since none is documented for the model

## Questions

### Which is better for AI agents, GLM-4.5-Air or Nemotron 3 Nano 30B A3B?

Nemotron 3 Nano 30B A3B scores 67.1 (B) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 3 of 5 scored categories. GLM-4.5-Air leads on risk.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-nano-30b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-nano-30b-a3b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "glm-4-5-air", "b": "nemotron-3-nano-30b-a3b"}`. From a terminal: `anchor compare glm-4-5-air nemotron-3-nano-30b-a3b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-4-5-air.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.json

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