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

> Qwen3-Coder-Next scores 57.6 (C) to Qwen3-Coder-30B-A3B-Instruct's 55.7 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-coder-next
- Markdown: https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-coder-next.md (~6,850 tokens)
- Slim: https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-coder-next.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-coder-next.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-Coder-Next scores 57.6 (C) on agent readiness against Qwen3-Coder-30B-A3B-Instruct's 55.7 (C), and leads in 2 of 5 scored categories. Qwen3-Coder-30B-A3B-Instruct leads on reach and risk. 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-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
- 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
- Risk, 55 against 50

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

### 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:
- Capability, 42 against 33
- Documentation and upkeep, 41 against 31

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


## Score by category

| Category | Weight | Qwen3-Coder-30B-A3B-Instruct | Qwen3-Coder-Next | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 33 | 42 | Qwen3-Coder-Next +9 |
| Reach | 25% (25 this run) | 90 | 85 | Qwen3-Coder-30B-A3B-Instruct +5 |
| Risk | 20% (20 this run) | 55 | 50 | Qwen3-Coder-30B-A3B-Instruct +5 |
| Agent interface | 10% (10 this run) | 75 | 75 | even |
| Documentation and upkeep | 10% (10 this run) | 31 | 41 | Qwen3-Coder-Next +10 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **55.7 · C** | **57.6 · C** | |

## Facts side by side

| Fact | Qwen3-Coder-30B-A3B-Instruct | Qwen3-Coder-Next |
| --- | --- | --- |
| 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 | 2026-02-03 |
| 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) | 2.8/5 (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-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.

## 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-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

## Questions

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

Qwen3-Coder-Next scores 57.6 (C) on agent readiness against Qwen3-Coder-30B-A3B-Instruct's 55.7 (C), and leads in 2 of 5 scored categories. Qwen3-Coder-30B-A3B-Instruct leads on reach and risk.


## For agents

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

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