# Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-122B-A10B

> Nemotron 3.5 Lightning 30B A3B and Qwen3.5-122B-A10B score within a point of each other for open-weight models you run yourself, 64.2 and 63.6 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-122b-a10b
- Markdown: https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-122b-a10b.md (~6,950 tokens)
- Slim: https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-122b-a10b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-122b-a10b.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 and Qwen3.5-122B-A10B score within a point of each other on agent readiness, 64.2 (B) and 63.6 (B). Qwen3.5-122B-A10B leads on capability. Both do open-weight models you run yourself.

- 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
- Qwen3.5-122B-A10B: grade B, 63.6/100, rank #22 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-122b-a10b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-122b-a10b.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

### 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 70
- Documentation and upkeep, 70 against 39

Watch for: No safety evaluation results published

### Qwen3.5-122B-A10B (B)

Good for: Agents with a 128 GB unified-memory machine or two large GPUs that want a capable vision-language model with a plain licence.

Ahead on:
- Capability, 62 against 34

Watch for: Too big for a single 64 GB machine even at 4 bits


## Score by category

| Category | Weight | Nemotron 3.5 Lightning 30B A3B | Qwen3.5-122B-A10B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 34 | 62 | Qwen3.5-122B-A10B +28 |
| Reach | 25% (25 this run) | 85 | 80 | Nemotron 3.5 Lightning 30B A3B +5 |
| Risk | 20% (20 this run) | 75 | 55 | Nemotron 3.5 Lightning 30B A3B +20 |
| Agent interface | 10% (10 this run) | 90 | 70 | Nemotron 3.5 Lightning 30B A3B +20 |
| Documentation and upkeep | 10% (10 this run) | 70 | 39 | Nemotron 3.5 Lightning 30B A3B +31 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **64.2 · B** | **63.6 · B** | |

## Facts side by side

| Fact | Nemotron 3.5 Lightning 30B A3B | Qwen3.5-122B-A10B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | NVIDIA | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | OpenMDW-1.1 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-08-11 | 2026-02-24 |
| 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 | 3/5 (4) | 3/5 (4) |

## Verdicts

**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.

**Qwen3.5-122B-A10B.** A strong mid-size cousin for coding, agent steps and reading images or video, with thinking you can switch off per request. Alibaba's 4-bit build needs about 95 GB, which fits one 128 GB unified-memory machine, and several providers host it. The main risk is silence, with no safety results, agent guidance or answers from Alibaba.

## Before you call either

### 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

### Qwen3.5-122B-A10B

1. Quickest start on a 128 GB machine is `ollama run qwen3.5:122b` (Q4_K_M, 81 GB of files, about 97 GB with context)
2. Alibaba's 4-bit build serves with `vllm serve Qwen/Qwen3.5-122B-A10B-GPTQ-Int4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --quantization moe_wna16`, though that kernel isn't documented for AMD gfx1151
3. Use the qwen3_coder tool parser and the qwen3 reasoning parser, as the card's commands do
4. Turn thinking off with `"chat_template_kwargs": {"enable_thinking": false}` in the request body, since it's on by default and `/think` and `/nothink` aren't supported
5. Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without
6. Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens
7. Add `--language-model-only` in vLLM to skip the vision encoder and free memory for the KV cache when you only need text
8. Validate any JSON it returns, since structured output isn't documented

## Questions

### Which is better for AI agents, Nemotron 3.5 Lightning 30B A3B or Qwen3.5-122B-A10B?

Nemotron 3.5 Lightning 30B A3B and Qwen3.5-122B-A10B score within a point of each other on agent readiness, 64.2 (B) and 63.6 (B). Qwen3.5-122B-A10B leads on capability.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-122b-a10b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-122b-a10b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-5-lightning-30b-a3b", "b": "qwen3-5-122b-a10b"}`. From a terminal: `anchor compare nemotron-3-5-lightning-30b-a3b qwen3-5-122b-a10b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-lightning-30b-a3b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-5-122b-a10b.json

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- [Ornith-1.5-35B-A3B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-122b-a10b.md)
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- [Qwen3-30B-A3B-Instruct-2507 vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-5-122b-a10b.md)
- [Qwen3-4B-Instruct-2507 vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-122b-a10b.md)
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- [Qwen3.5-122B-A10B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-coder-next.md)
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- [Qwen3.5-122B-A10B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwopus3-8-27b-flash-v2.md)
- [Qwen3.5-122B-A10B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-smollm3-3b.md)
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- [Qwen3.5-122B-A10B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-27b.md)
- [Qwen3.5-122B-A10B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-flash-next.md)
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