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

> Qwen3-VL-30B-A3B-Instruct and Qwen3-Coder-Next score within a point of each other for open-weight models you run yourself, 58 and 57.6 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-30b-a3b-instruct
- Markdown: https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-30b-a3b-instruct.md (~6,650 tokens)
- Slim: https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-30b-a3b-instruct.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-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

Qwen3-VL-30B-A3B-Instruct and Qwen3-Coder-Next score within a point of each other on agent readiness, 58 (C) and 57.6 (C). Qwen3-Coder-Next leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.

- Qwen3-Coder-Next: grade C, 57.6/100, rank #70 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-coder-next.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-coder-next.json
- Qwen3-VL-30B-A3B-Instruct: grade C, 58/100, rank #68 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-vl-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

## Which one, for what

### Qwen3-Coder-Next (C)

Good for: Agents that want a local coding model for long tool-calling loops on a single 64 GB to 128 GB machine.

Ahead on:
- Agent interface, 75 against 60
- Documentation and upkeep, 41 against 36

Watch for: No LICENSE file anywhere and a broken licence link on the card

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

Good for: An agent with 24 GB or more to spare that wants a faster, stronger local reader of images, documents and video than the 8B model

Ahead on:
- Reach, 95 against 85
- Risk, 55 against 50

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


## Score by category

| Category | Weight | Qwen3-Coder-Next | Qwen3-VL-30B-A3B-Instruct | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 42 | 39 | Qwen3-Coder-Next +3 |
| Reach | 25% (25 this run) | 85 | 95 | Qwen3-VL-30B-A3B-Instruct +10 |
| Risk | 20% (20 this run) | 50 | 55 | Qwen3-VL-30B-A3B-Instruct +5 |
| Agent interface | 10% (10 this run) | 75 | 60 | Qwen3-Coder-Next +15 |
| Documentation and upkeep | 10% (10 this run) | 41 | 36 | Qwen3-Coder-Next +5 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **57.6 · C** | **58 · C** | |

## Facts side by side

| Fact | Qwen3-Coder-Next | Qwen3-VL-30B-A3B-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 | 2026-02-03 | 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) | 3/5 (4) |

## Verdicts

**Qwen3-Coder-Next.** A fast coding cousin for agent harnesses, with 3B active parameters and tool calling trained across many scaffold formats for Claude Code, Cline or Qwen Code loops. Alibaba's 4-bit GGUF needs about 58 GB, fitting one 64 GB machine, and two providers host it. The main risk is neglect, with no change since February, no replies and no safety results.

**Qwen3-VL-30B-A3B-Instruct.** A mid-sized vision-language cousin for screenshots, documents, charts and video that runs fast for its size, with about 3B active parameters. It needs about 23 GB for Alibaba's Q4_K_M GGUF with its vision file, loads in every major runtime under plain Apache 2.0, and three providers host it. With no published safety work, add guardrails before it drives a GUI.

## Before you call either

### Qwen3-Coder-Next

1. Quickest start is `ollama run qwen3-coder-next` (Q4_K_M, 52 GB of files)
2. On our reference machine Alibaba's Q8_0 GGUF (84.8 GB, about 102 GB in use) also fits, run with `llama-server -m Qwen3-Coder-Next-Q8_0/Qwen3-Coder-Next-Q8_0-00001-of-00004.gguf --jinja -ngl 99 -fa on -c 131072`
3. Serve with `vllm serve Qwen/Qwen3-Coder-Next --enable-auto-tool-choice --tool-call-parser qwen3_coder` (vLLM 0.15.0 or later), using the built-in parser rather than the repository's qwen3coder_tool_parser_vllm.py, which fails to import on newer vLLM per discussion 36
4. The card's commands use tensor parallel 2 though the text says 4 GPUs, and it suggests cutting the context to 32,768 if the server fails to start
5. There's no thinking mode, so don't pass `enable_thinking` or wait for reasoning_content
6. Sampling per the card is temperature 1.0, top_p 0.95 and top_k 40
7. The GGUF card's llama.cpp example path omits the quant from the file name; the real files are named like Qwen3-Coder-Next-Q4_K_M-00001-of-00004.gguf
8. Validate any JSON it returns, since structured output isn't documented

### Qwen3-VL-30B-A3B-Instruct

1. Start it with `llama-server -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:30b-a3b-instruct`
2. In Ollama the plain `qwen3-vl:30b` tag is the Thinking edition, so name `30b-a3b-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 shipped sampling, temperature 0.7, top_p 0.8 and top_k 20, from generation_config.json
6. Validate JSON output yourself, since structured output isn't documented
7. vLLM needs 0.11.0 or later, Transformers 4.57.0 or later and llama.cpp b6907 or later

## Questions

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

Qwen3-VL-30B-A3B-Instruct and Qwen3-Coder-Next score within a point of each other on agent readiness, 58 (C) and 57.6 (C). Qwen3-Coder-Next leads on agent interface and documentation and upkeep.


## For agents

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

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