# Nemotron 3 Ultra 550B A55B vs Qwen3.5-0.8B

> Nemotron 3 Ultra 550B A55B scores 68.8 (B) to Qwen3.5-0.8B's 46.7 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-0-8b
- Markdown: https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-0-8b.md (~6,550 tokens)
- Slim: https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-0-8b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-0-8b.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 Ultra 550B A55B scores 68.8 (B) on agent readiness against Qwen3.5-0.8B's 46.7 (D), and leads in 4 of 5 scored categories. Qwen3.5-0.8B leads on reach. Both do open-weight models you run yourself.

- Nemotron 3 Ultra 550B A55B: grade B, 68.8/100, rank #9 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-ultra-550b-a55b.json
- Qwen3.5-0.8B: grade D, 46.7/100, rank #97 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-0-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-0-8b.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 Ultra 550B A55B (B)

Good for: Agents with a multi-GPU Blackwell or Hopper node, or using a hosted endpoint, that want NVIDIA's strongest open model for long agent runs

Ahead on:
- Capability, 73 against 17
- Risk, 70 against 55
- Agent interface, 90 against 60
- Documentation and upkeep, 65 against 37

Watch for: The smallest build is 352 GB and needs at least four B200-class GPUs

### Qwen3.5-0.8B (D)

Good for: Builders who want a near-free local helper or fine-tuning base for narrow tasks on a phone-class device, laptop or CPU

Ahead on:
- Reach, 80 against 55

Watch for: No quantised build from Alibaba and no inference provider found


## Score by category

| Category | Weight | Nemotron 3 Ultra 550B A55B | Qwen3.5-0.8B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 73 | 17 | Nemotron 3 Ultra 550B A55B +56 |
| Reach | 25% (25 this run) | 55 | 80 | Qwen3.5-0.8B +25 |
| Risk | 20% (20 this run) | 70 | 55 | Nemotron 3 Ultra 550B A55B +15 |
| Agent interface | 10% (10 this run) | 90 | 60 | Nemotron 3 Ultra 550B A55B +30 |
| Documentation and upkeep | 10% (10 this run) | 65 | 37 | Nemotron 3 Ultra 550B A55B +28 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **68.8 · B** | **46.7 · D** | |

## Facts side by side

| Fact | Nemotron 3 Ultra 550B A55B | Qwen3.5-0.8B |
| --- | --- | --- |
| 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 | 2026-06-04 | 2026-03-02 |
| 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) | 2.8/5 (4) |

## Verdicts

**Nemotron 3 Ultra 550B A55B.** NVIDIA's frontier-scale agent cousin with trained parallel tool calls, up to a 1M-token context and training against prompt injection through tool output. It needs at least four Blackwell GPUs for the 352 GB NVFP4 build, so most agents will use NVIDIA's API or a provider. The main risks are serving commands that run repository code and no published safety results.

**Qwen3.5-0.8B.** A tiny vision-language cousin for prototypes, fine-tuning and light local steps such as routing, tagging or reading an image, running in under 2 GB in every major runtime under plain Apache 2.0. Keep thinking off, since the card warns it loops. The main risk is neglect, with no hosted option, safety results or answers from Alibaba since March.

## Before you call either

### Nemotron 3 Ultra 550B A55B

1. It doesn't fit the 128 GB reference machine at any precision. Call NVIDIA's API (model nvidia/nemotron-3-ultra-550b-a55b) or a provider instead
2. With vLLM serve the NVFP4 build with `--trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on 4 × B200 or more
3. With SGLang use `--reasoning-parser nemotron_3`, not the BF16 card's `nemotron_v3`, which SGLang doesn't register
4. On SGLang with tools, pass `chat_template_kwargs` with `enable_thinking` true and `force_nonempty_content` true, as the card says, or reasoning and calls won't both parse
5. Thinking is on by default. Pass `enable_thinking: false` for short tasks or `medium_effort: true` to cut reasoning tokens
6. Use `temperature=1.0` and `top_p=0.95`, as the generation config sets

### Qwen3.5-0.8B

1. Smallest start is `ollama run qwen3.5:0.8b`, or `qwen3.5:0.8b-q8_0` at 1.0 GB
2. Full precision `vllm serve Qwen/Qwen3.5-0.8B --port 8000 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder`
3. Thinking is off by default; if you turn it on with `"chat_template_kwargs": {"enable_thinking": true}`, stream and cut off runaway output, since the card warns of thinking loops, and add `--reasoning-parser qwen3` in vLLM or SGLang to split the reasoning out
4. There's no generation_config.json, so pass the card's sampling yourself, for example `temperature=1.0, top_p=1.0, top_k=20, presence_penalty=2.0` for text without thinking
5. Use it for narrow steps or as a fine-tuning base, not as the main agent, as the card itself suggests
6. Validate any JSON it returns, since structured output isn't documented

## Questions

### Which is better for AI agents, Nemotron 3 Ultra 550B A55B or Qwen3.5-0.8B?

Nemotron 3 Ultra 550B A55B scores 68.8 (B) on agent readiness against Qwen3.5-0.8B's 46.7 (D), and leads in 4 of 5 scored categories. Qwen3.5-0.8B leads on reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-0-8b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-0-8b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-ultra-550b-a55b", "b": "qwen3-5-0-8b"}`. From a terminal: `anchor compare nemotron-3-ultra-550b-a55b qwen3-5-0-8b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nemotron-3-ultra-550b-a55b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-5-0-8b.json

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- [Nemotron 3 Ultra 550B A55B vs SmolLM3-3B](https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-smollm3-3b.md)
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- [Ornith-1.5-9B vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-0-8b.md)
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