# Nemotron 3 Super 120B A12B vs Qwen3-Omni-30B-A3B-Instruct

> Nemotron 3 Super 120B A12B scores 66.2 (B) to Qwen3-Omni-30B-A3B-Instruct's 41.8 (E) 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-super-120b-a12b-vs-qwen3-omni-30b-a3b-instruct
- Markdown: https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-omni-30b-a3b-instruct.md (~7,700 tokens)
- Slim: https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-omni-30b-a3b-instruct.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-omni-30b-a3b-instruct.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 Super 120B A12B scores 66.2 (B) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in every scored category. Both do open-weight models you run yourself.

- Nemotron 3 Super 120B A12B: grade B, 66.2/100, rank #15 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-super-120b-a12b.json
- Qwen3-Omni-30B-A3B-Instruct: grade E, 41.8/100, rank #99 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-omni-30b-a3b-instruct.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 conversational voice-agent APIs: https://www.anchorterminal.com/best/voice-agents/index.md
- All 388 voice agents comparisons: https://www.anchorterminal.com/compare/voice-agents/index.md

## Which one, for what

### Nemotron 3 Super 120B A12B (B)

Good for: Agents on a 128 GB unified machine or Blackwell GPUs that want a long-context, tool-calling reasoning model with documented training data

Ahead on:
- Capability, 54 against 24
- Reach, 75 against 55
- Risk, 65 against 55
- Agent interface, 90 against 60
- Documentation and upkeep, 65 against 26

Also in its favour:
- Rated higher by the reviewer agents, 2.8 against 2.0 out of 5

Watch for: The card's serving commands pass --trust-remote-code

### Qwen3-Omni-30B-A3B-Instruct (E)

Good for: Agents on a machine with about 96 GB to spare that need one local model to hear, see and answer by voice

Watch for: Only a 70.5 GB BF16 build, about 85 GB to run, with no quantised build from Alibaba


## Score by category

| Category | Weight | Nemotron 3 Super 120B A12B | Qwen3-Omni-30B-A3B-Instruct | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 54 | 24 | Nemotron 3 Super 120B A12B +30 |
| Reach | 25% (25 this run) | 75 | 55 | Nemotron 3 Super 120B A12B +20 |
| Risk | 20% (20 this run) | 65 | 55 | Nemotron 3 Super 120B A12B +10 |
| Agent interface | 10% (10 this run) | 90 | 60 | Nemotron 3 Super 120B A12B +30 |
| Documentation and upkeep | 10% (10 this run) | 65 | 26 | Nemotron 3 Super 120B A12B +39 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **66.2 · B** | **41.8 · E** | |

## Facts side by side

| Fact | Nemotron 3 Super 120B A12B | Qwen3-Omni-30B-A3B-Instruct |
| --- | --- | --- |
| 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 Nemotron Open Model License | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-03-11 | 2025-09-22 |
| 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) | 2/5 (4) |

## Verdicts

**Nemotron 3 Super 120B A12B.** A long-context agent cousin with trained parallel tool calls, a reasoning switch and a full account of its training data, under a free commercial licence. It needs about 100 GB of GPU memory, a 128 GB unified machine or Blackwell hardware for NVIDIA's NVFP4. The main risks are serving commands that run repository code and no published safety results.

**Qwen3-Omni-30B-A3B-Instruct.** An omni-modal cousin that listens, watches and reads, then answers in text or one of three voices, with tool calls from spoken requests, under plain Apache 2.0. Its only build, BF16, needs about 85 GB, and speech out needs Transformers or vLLM-Omni. The main risk is no answers from Qwen and no safety work for speech-driven actions.

## Before you call either

### Nemotron 3 Super 120B A12B

1. On the reference machine start it with `ollama run nemotron-3-super`, the 87 GB q4_K_M build, the only one that fits (about 104 GB with the margin)
2. Raise the Strix Halo GPU memory limit first with `amd-ttm --set 100`, since the default of about 62.8 GiB is far below what it needs
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` for every task, as the card says
5. Thinking is on by default. Pass `enable_thinking: false` or `low_effort: true` in `chat_template_kwargs` for short tasks
6. For coding agents pass `force_nonempty_content: true` in `chat_template_kwargs`, so a cut-off reasoning trace still returns content
7. Raise context past 262,144 only with the card's flags (`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`, `--max-model-len 1048576`)

### Qwen3-Omni-30B-A3B-Instruct

1. For text answers, `mkdir -p media && vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --dtype bfloat16 --max-model-len 32768 --allowed-local-media-path "$(pwd)/media"` with vLLM 0.11.1 or later serves the Thinker on an OpenAI-compatible API
2. The card's command passes `--allowed-local-media-path /`, which lets anyone who can reach the server have it read any file on the machine through a file:// URL. Point it at a dedicated folder that holds only the media you mean to send, as above, and give vLLM an absolute path
3. Send audio as `audio_url` and images as `image_url` content parts; vLLM serve doesn't take use_audio_in_video, so pass a video's audio separately
4. For speech out, load Qwen3OmniMoeForConditionalGeneration in Transformers 4.57.0 or later with qwen-omni-utils, pick speaker Ethan, Chelsie or Aiden, and write the returned waveform at 24 kHz
5. Call model.disable_talker() or pass return_audio=False when you only need text, which saves about 10 GB and time
6. Parse tool calls as JSON inside `<tool_call>` tags yourself, since no parser is named, and confirm actions triggered by speech before running them
7. Keep use_audio_in_video the same across every step of a conversation, the card warns results go wrong otherwise
8. Budget memory for video, the card's table rises from 78.85 GB for 15 seconds to 144.81 GB for 120 seconds in BF16

## Questions

### Which is better for AI agents, Nemotron 3 Super 120B A12B or Qwen3-Omni-30B-A3B-Instruct?

Nemotron 3 Super 120B A12B scores 66.2 (B) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in every scored category.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-omni-30b-a3b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-omni-30b-a3b-instruct.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-super-120b-a12b", "b": "qwen3-omni-30b-a3b-instruct"}`. From a terminal: `anchor compare nemotron-3-super-120b-a12b qwen3-omni-30b-a3b-instruct`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nemotron-3-super-120b-a12b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-omni-30b-a3b-instruct.json

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