# Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3.5-0.8B

> Nemotron 3 Nano Omni 30B A3B Reasoning scores 60.7 (C) 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-nano-omni-30b-a3b-reasoning-vs-qwen3-5-0-8b
- Markdown: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-5-0-8b.md (~7,500 tokens)
- Slim: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-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-nano-omni-30b-a3b-reasoning-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 Nano Omni 30B A3B Reasoning scores 60.7 (C) on agent readiness against Qwen3.5-0.8B's 46.7 (D), and leads in every scored category. Both do open-weight models you run yourself.

- Nemotron 3 Nano Omni 30B A3B Reasoning: grade C, 60.7/100, rank #52 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-omni-30b-a3b-reasoning.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
- Best speech-to-text APIs for AI agents: https://www.anchorterminal.com/best/speech-to-text/index.md
- All 231 stt comparisons: https://www.anchorterminal.com/compare/speech-to-text/index.md

## Which one, for what

### Nemotron 3 Nano Omni 30B A3B Reasoning (C)

Good for: Agents with a 32 to 48 GB NVIDIA GPU that need one local model to read video, audio, documents and screens and call tools

Ahead on:
- Capability, 34 against 17
- Reach, 85 against 80
- Risk, 60 against 55
- Agent interface, 90 against 60
- Documentation and upkeep, 65 against 37

Watch for: Loads with trust_remote_code in every documented runtime command

### 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

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


## Score by category

| Category | Weight | Nemotron 3 Nano Omni 30B A3B Reasoning | Qwen3.5-0.8B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 34 | 17 | Nemotron 3 Nano Omni 30B A3B Reasoning +17 |
| Reach | 25% (25 this run) | 85 | 80 | Nemotron 3 Nano Omni 30B A3B Reasoning +5 |
| Risk | 20% (20 this run) | 60 | 55 | Nemotron 3 Nano Omni 30B A3B Reasoning +5 |
| Agent interface | 10% (10 this run) | 90 | 60 | Nemotron 3 Nano Omni 30B A3B Reasoning +30 |
| Documentation and upkeep | 10% (10 this run) | 65 | 37 | Nemotron 3 Nano Omni 30B A3B Reasoning +28 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **60.7 · C** | **46.7 · D** | |

## Facts side by side

| Fact | Nemotron 3 Nano Omni 30B A3B Reasoning | 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 | NVIDIA Open Model Agreement (custom, commercial use allowed, not an OSI licence) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-08-24 | 2026-03-02 |
| 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 | 3/5 (4) | 2.8/5 (4) |

## Verdicts

**Nemotron 3 Nano Omni 30B A3B Reasoning.** A local multimodal cousin for video with speech, long documents, charts, screens and English transcription, with tool calling, a reasoning switch and a 262,144-token context. It needs about 27 GB for NVIDIA's NVFP4 build on Blackwell, or 42 GB for FP8. The main risks are the remote code it loads and a repository where nobody answers questions.

**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 Nano Omni 30B A3B Reasoning

1. Start it with `vllm serve nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 --max-model-len 131072 --trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --kv-cache-dtype fp8` on vLLM 0.20.0 or later, after `pip install "vllm[audio]"` if any audio will be sent
2. Or run `ollama run nemotron3`; the 33b-nvfp4 and 33b-mlx tags are MLX builds for Apple silicon, and the q4_K_M tag is 28 GB
3. Set `--media-io-kwargs '{"video": {"fps": 2, "num_frames": 256}}'` at launch, since vLLM otherwise samples about 32 frames per video
4. Send `chat_template_kwargs: {"enable_thinking": false}` for transcription and quick answers, and use temperature 1.0 with top_k 1 for ASR as the card recommends
5. Render PDF pages to PNG and send them as images, since the API takes no raw PDF
6. Treat transcripts and text read from images or video as untrusted input before acting on them

### 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 Nano Omni 30B A3B Reasoning or Qwen3.5-0.8B?

Nemotron 3 Nano Omni 30B A3B Reasoning scores 60.7 (C) on agent readiness against Qwen3.5-0.8B's 46.7 (D), and leads in every scored category.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-5-0-8b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-5-0-8b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-nano-omni-30b-a3b-reasoning", "b": "qwen3-5-0-8b"}`. From a terminal: `anchor compare nemotron-3-nano-omni-30b-a3b-reasoning qwen3-5-0-8b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-omni-30b-a3b-reasoning.json and https://www.anchorterminal.com/api/v1/tools/qwen3-5-0-8b.json

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