# Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-2B-Instruct

> Qwen3-VL-2B-Instruct scores 61 (C) 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/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-2b-instruct
- Markdown: https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-2b-instruct.md (~7,400 tokens)
- Slim: https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-2b-instruct.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-2b-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

Qwen3-VL-2B-Instruct scores 61 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in 2 of 4 scored categories. Both do open-weight models you run yourself.

- 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
- Qwen3-VL-2B-Instruct: grade C, 61/100, rank #45 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-vl-2b-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

### 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

### Qwen3-VL-2B-Instruct (C)

Good for: An agent on a laptop or small edge device that needs a local model to read images, documents and video in about 2 GB

Ahead on:
- Reach, 95 against 55
- Documentation and upkeep, 31 against 26

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

Watch for: No safety evaluation, agent-use guidance or security contact


## Score by category

| Category | Weight | Qwen3-Omni-30B-A3B-Instruct | Qwen3-VL-2B-Instruct | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 55 | 95 | Qwen3-VL-2B-Instruct +40 |
| Risk | 20% (20 this run) | 55 | 55 | even |
| Agent interface | 10% (10 this run) | 60 | 60 | even |
| Documentation and upkeep | 10% (10 this run) | 26 | 31 | Qwen3-VL-2B-Instruct +5 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **41.8 · E** | **61 · C** | |

## Facts side by side

| Fact | Qwen3-Omni-30B-A3B-Instruct | Qwen3-VL-2B-Instruct |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Alibaba (Qwen team) | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-09-22 | 2025-11-01 |
| 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 | 2/5 (4) | 2.8/5 (4) |

## Verdicts

**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.

**Qwen3-VL-2B-Instruct.** The smallest Qwen3-VL cousin, for reading screenshots, documents, charts and video on a laptop or edge box, running in about 2 GB from Alibaba's Q4_K_M GGUF in every major runtime under plain Apache 2.0. Qwen hasn't answered repository questions or published safety work, so keep a human or a check in the loop before it drives a GUI.

## Before you call either

### 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

### Qwen3-VL-2B-Instruct

1. Start it with `llama-server -hf Qwen/Qwen3-VL-2B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:2b-instruct`
2. In Ollama the plain `qwen3-vl:2b` tag is the Thinking edition, so name `2b-instruct`
3. With the GGUF, load the mmproj vision file too, or images won't be read
4. Parse tool calls as JSON inside `<tool_call>` tags, or use SGLang's `--tool-call-parser qwen`
5. Use the card's sampling, `temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5` for vision and `temperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0` for text
6. Validate JSON output yourself, since structured output isn't documented
7. vLLM needs 0.11.0 or later and Transformers 4.57.0 or later

## Questions

### Which is better for AI agents, Qwen3-Omni-30B-A3B-Instruct or Qwen3-VL-2B-Instruct?

Qwen3-VL-2B-Instruct scores 61 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in 2 of 4 scored categories.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-2b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-2b-instruct.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "qwen3-omni-30b-a3b-instruct", "b": "qwen3-vl-2b-instruct"}`. From a terminal: `anchor compare qwen3-omni-30b-a3b-instruct qwen3-vl-2b-instruct`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/qwen3-omni-30b-a3b-instruct.json and https://www.anchorterminal.com/api/v1/tools/qwen3-vl-2b-instruct.json

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