# GLM-4.7-Flash vs Nemotron 3 Nano 4B

> GLM-4.7-Flash scores 63 (B) to Nemotron 3 Nano 4B's 56.8 (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-4-7-flash-vs-nemotron-3-nano-4b
- Markdown: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-4b.md (~6,300 tokens)
- Slim: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-4b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-4b.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-4.7-Flash scores 63 (B) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 2 of 5 scored categories. Nemotron 3 Nano 4B leads on reach, risk and documentation and upkeep. 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
- Nemotron 3 Nano 4B: grade C, 56.8/100, rank #73 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.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:
- Capability, 59 against 25
- 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

### Nemotron 3 Nano 4B (C)

Good for: Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English

Ahead on:
- Reach, 90 against 80
- Risk, 65 against 55
- Documentation and upkeep, 55 against 28

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

Watch for: The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository


## Score by category

| Category | Weight | GLM-4.7-Flash | Nemotron 3 Nano 4B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 59 | 25 | GLM-4.7-Flash +34 |
| Reach | 25% (25 this run) | 80 | 90 | Nemotron 3 Nano 4B +10 |
| Risk | 20% (20 this run) | 55 | 65 | Nemotron 3 Nano 4B +10 |
| Agent interface | 10% (10 this run) | 85 | 70 | GLM-4.7-Flash +15 |
| Documentation and upkeep | 10% (10 this run) | 28 | 55 | Nemotron 3 Nano 4B +27 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **63 · B** | **56.8 · C** | |

## Facts side by side

| Fact | GLM-4.7-Flash | Nemotron 3 Nano 4B |
| --- | --- | --- |
| 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 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-01-29 | 2026-03-16 |
| 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) | 3.3/5 (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.

**Nemotron 3 Nano 4B.** A tiny reasoning cousin with trained tool calls, a thinking switch and 262,144 tokens of context, running in about 4 GB from NVIDIA's own GGUF on almost any machine. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files untouched and questions unanswered since March.

## 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

### Nemotron 3 Nano 4B

1. Quickest start is `ollama run nemotron-3-nano:4b`, a 2.8 GB build
2. With llama.cpp use `llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja`, NVIDIA's own Q4_K_M file
3. With vLLM, download nano_v3_reasoning_parser.py from the repository first and serve with `--enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`
4. The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead
5. Thinking is on by default. Pass `enable_thinking=False` to the chat template for short tasks
6. Use temperature 1.0 and top_p 0.95 for reasoning and temperature 0.6 with top_p 0.95 for tool calling, as the card says
7. It's tuned for English only, and validate JSON yourself, since structured output isn't documented

## Questions

### Which is better for AI agents, GLM-4.7-Flash or Nemotron 3 Nano 4B?

GLM-4.7-Flash scores 63 (B) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 2 of 5 scored categories. Nemotron 3 Nano 4B leads on reach, risk and documentation and upkeep.


## For agents

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

## Other comparisons with GLM-4.7-Flash or Nemotron 3 Nano 4B

- [Agents-A1-4B vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-7-flash.md)
- [Agents-A1-4B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-4b.md)
- [Apertus v1.5 8B vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-7-flash.md)
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- [Cosmos-Reason2-2B vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-4-7-flash.md)
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