# Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-32B-Instruct

> Qwen3-VL-32B-Instruct and Qwen3-Coder-30B-A3B-Instruct score within a point of each other for open-weight models you run yourself, 56.4 and 55.7 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-32b-instruct
- Markdown: https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-32b-instruct.md (~6,950 tokens)
- Slim: https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-32b-instruct.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-32b-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-32B-Instruct and Qwen3-Coder-30B-A3B-Instruct score within a point of each other on agent readiness, 56.4 (C) and 55.7 (C). Qwen3-Coder-30B-A3B-Instruct leads on reach and agent interface. Both do open-weight models you run yourself.

- Qwen3-Coder-30B-A3B-Instruct: grade C, 55.7/100, rank #78 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-coder-30b-a3b-instruct.json
- Qwen3-VL-32B-Instruct: grade C, 56.4/100, rank #75 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-vl-32b-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

## Which one, for what

### Qwen3-Coder-30B-A3B-Instruct (C)

Good for: Coding agents and local coding tools that want a fast, capable open model on one 24 GB GPU or a 32 GB Mac, with a plain licence.

Ahead on:
- Reach, 90 against 85
- Agent interface, 75 against 60

Watch for: No safety results, prompt-injection guidance or security contact, though it's meant to run commands

### Qwen3-VL-32B-Instruct (C)

Good for: An agent with a 32 GB GPU or more that wants the strongest dense Qwen3-VL model for images, documents and video

Ahead on:
- Capability, 43 against 33

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


## Score by category

| Category | Weight | Qwen3-Coder-30B-A3B-Instruct | Qwen3-VL-32B-Instruct | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 33 | 43 | Qwen3-VL-32B-Instruct +10 |
| Reach | 25% (25 this run) | 90 | 85 | Qwen3-Coder-30B-A3B-Instruct +5 |
| Risk | 20% (20 this run) | 55 | 55 | even |
| Agent interface | 10% (10 this run) | 75 | 60 | Qwen3-Coder-30B-A3B-Instruct +15 |
| Documentation and upkeep | 10% (10 this run) | 31 | 31 | even |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **55.7 · C** | **56.4 · C** | |

## Facts side by side

| Fact | Qwen3-Coder-30B-A3B-Instruct | Qwen3-VL-32B-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 | 2025-12-03 | 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 | 2.8/5 (4) | 2.5/5 (4) |

## Verdicts

**Qwen3-Coder-30B-A3B-Instruct.** A fast coding cousin, with 3.3B of 30.5B parameters active per token, for agentic coding tools, repository reading and fill-in-the-middle without a thinking step. It needs about 21 to 23 GB at 4 bits, and several providers host it. The main risk is that it runs commands for agents with no safety results or guidance, and Alibaba doesn't answer users.

**Qwen3-VL-32B-Instruct.** The largest dense Qwen3-VL cousin, for harder work on screenshots, documents, charts and video, needing about 25 GB from Alibaba's Q4_K_M GGUF with its vision file, in every major runtime under plain Apache 2.0. It has no thinking mode or published safety work, and only one provider was found serving it, so plan to run it yourself.

## Before you call either

### Qwen3-Coder-30B-A3B-Instruct

1. Quickest start is `ollama run qwen3-coder:30b`, 19 GB
2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, 61 GB of weights fits the GPU share
3. Use the qwen3_coder parser in vLLM or SGLang, not hermes, since calls come back as function and parameter blocks rather than JSON
4. Pass tool-call arguments in the history as objects, the template iterates them as a mapping
5. Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and repetition_penalty 1.05, with up to 65,536 output tokens
6. For code completion use the fill-in-the-middle tokens `<|fim_prefix|>`, `<|fim_suffix|>` and `<|fim_middle|>` from Qwen's README
7. Sandbox the commands and files it touches, since nothing from Alibaba covers prompt injection

### Qwen3-VL-32B-Instruct

1. Start it with `llama-server -hf Qwen/Qwen3-VL-32B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:32b-instruct`
2. In Ollama the plain `qwen3-vl:32b` tag is the Thinking edition, so name `32b-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
8. If you pulled the FP8 build before 22 October 2025, pull it again, since the earlier files were the Thinking edition's

## Questions

### Which is better for AI agents, Qwen3-Coder-30B-A3B-Instruct or Qwen3-VL-32B-Instruct?

Qwen3-VL-32B-Instruct and Qwen3-Coder-30B-A3B-Instruct score within a point of each other on agent readiness, 56.4 (C) and 55.7 (C). Qwen3-Coder-30B-A3B-Instruct leads on reach and agent interface.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-32b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-32b-instruct.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "qwen3-coder-30b-a3b-instruct", "b": "qwen3-vl-32b-instruct"}`. From a terminal: `anchor compare qwen3-coder-30b-a3b-instruct qwen3-vl-32b-instruct`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/qwen3-coder-30b-a3b-instruct.json and https://www.anchorterminal.com/api/v1/tools/qwen3-vl-32b-instruct.json

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