# GLM-4.7-Flash vs Nemotron 3.5 Lightning 30B A3B

> Nemotron 3.5 Lightning 30B A3B scores 64.2 (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-nemotron-3-5-lightning-30b-a3b
- Markdown: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-5-lightning-30b-a3b.md (~6,750 tokens)
- Slim: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-5-lightning-30b-a3b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-5-lightning-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 3.5 Lightning 30B A3B scores 64.2 (B) on agent readiness against GLM-4.7-Flash's 63 (B), and leads in 4 of 5 scored categories. GLM-4.7-Flash leads on capability. 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 3.5 Lightning 30B A3B: grade B, 64.2/100, rank #21 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-lightning-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-4.7-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 34

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

### Nemotron 3.5 Lightning 30B A3B (B)

Good for: Agents on one 24 to 32 GB GPU or Mac that want a fast tool-calling reasoning model with long context and a permissive licence

Ahead on:
- Reach, 85 against 80
- Risk, 75 against 55
- Agent interface, 90 against 85
- Documentation and upkeep, 70 against 28

Watch for: No safety evaluation results published


## Score by category

| Category | Weight | GLM-4.7-Flash | Nemotron 3.5 Lightning 30B A3B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 59 | 34 | GLM-4.7-Flash +25 |
| Reach | 25% (25 this run) | 80 | 85 | Nemotron 3.5 Lightning 30B A3B +5 |
| Risk | 20% (20 this run) | 55 | 75 | Nemotron 3.5 Lightning 30B A3B +20 |
| Agent interface | 10% (10 this run) | 85 | 90 | Nemotron 3.5 Lightning 30B A3B +5 |
| Documentation and upkeep | 10% (10 this run) | 28 | 70 | Nemotron 3.5 Lightning 30B A3B +42 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **63 · B** | **64.2 · B** | |

## Facts side by side

| Fact | GLM-4.7-Flash | Nemotron 3.5 Lightning 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 | OpenMDW-1.1 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-01-29 | 2026-08-11 |
| 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) | 3/5 (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 3.5 Lightning 30B A3B.** A small agent cousin, 3B active parameters, with trained parallel tool calls, a thinking switch and up to 1M tokens of context, under a permissive licence. It runs in about 23 GB from the GGUF or Ollama builds, and NVIDIA's NVFP4 build needs NVIDIA GPUs. The main risks are no published safety results and one answered question across two repositories.

## 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 3.5 Lightning 30B A3B

1. Quickest start is `ollama run nemotron-3.5-lightning`, about 19 to 25 GB of files depending on the platform
2. On the reference machine use `llama-server -hf ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_0 --jinja`, since the card's Q4_K_M tag doesn't exist there and NVFP4 needs NVIDIA GPUs
3. With vLLM serve it with `--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` (SGLang uses `--reasoning-parser nemotron_3`)
4. Use `temperature=1.0` and `top_p=0.95`, as the card says
5. Thinking is on by default. Pass `chat_template_kwargs` with `enable_thinking` false for short tasks
6. For coding agents pass `force_nonempty_content: true` in `chat_template_kwargs`, which vLLM's nemotron_v3 parser reads so a cut-off reasoning trace still returns content
7. Go past 262,144 tokens only with the card's flags (`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`, `--max-model-len 1048576`) and enough memory

## Questions

### Which is better for AI agents, GLM-4.7-Flash or Nemotron 3.5 Lightning 30B A3B?

Nemotron 3.5 Lightning 30B A3B scores 64.2 (B) on agent readiness against GLM-4.7-Flash's 63 (B), and leads in 4 of 5 scored categories. GLM-4.7-Flash leads on capability.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-5-lightning-30b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-5-lightning-30b-a3b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "glm-4-7-flash", "b": "nemotron-3-5-lightning-30b-a3b"}`. From a terminal: `anchor compare glm-4-7-flash nemotron-3-5-lightning-30b-a3b`
- 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-3-5-lightning-30b-a3b.json

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