Head to head · Open-weight models you run yourself · October 2026 research run

Qwen3.5-4B vs Qwen3-Coder-Next

Qwen3.5-4B scores 61.2 (C) on agent readiness against Qwen3-Coder-Next's 57.6 (C), and leads in 2 of 5 scored categories. Qwen3-Coder-Next leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 2870 open weights comparisons

Qwen3.5-4BAlibaba (Qwen team) · Open-weight model C61.2/100#33 of 100 open-weight models Qwen3-Coder-NextAlibaba (Qwen team) · Open-weight model C57.6/100#70 of 100 open-weight models

Which one, for what

Qwen3.5-4B C

Best for Agents that want a cheap local helper for tool calls, short reasoning and reading images on a laptop or small GPU

Ahead on

Capability, 53 against 42
Risk, 55 against 50

Watch for

No quantised build from Alibaba for this size

Qwen3-Coder-Next C

Best 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 70
Documentation and upkeep, 41 against 34

Watch for

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

Score by category

Category	Weight this run	Qwen3.5-4B	Qwen3-Coder-Next	Edge
Capability	35%35	53	42	Qwen3.5-4B +11
Reach	25%25	85	85	even
Risk	20%20	55	50	Qwen3.5-4B +5
Agent interface	10%10	70	75	Qwen3-Coder-Next +5
Documentation and upkeep	10%10	34	41	Qwen3-Coder-Next +7
Negative events	≤15	0	0
Total		C 61.2/100	C 57.6/100

Facts side by side

Fact	Qwen3.5-4B	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	2026-03-02	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	3/5 (4)	2.8/5 (4)

Verdicts

Qwen3.5-4B

A small vision-language cousin for tool calls, light agent steps and image or video reading on almost any machine, with switchable thinking. Ollama's 3.3 GB build or the 9.3 GB BF16 files run in every major runtime under plain Apache 2.0. The main risks are no safety results, no structured-output guarantee and no answers from Alibaba since March.

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

Qwen3.5-4B

Smallest start is ollama run qwen3.5:4b (Q4_K_M, about 4 GB with context)
Full precision vllm serve Qwen/Qwen3.5-4B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder
Turn thinking off with "chat_template_kwargs": {"enable_thinking": false} in the request body, since it's on by default and /think and /nothink aren't supported
There's no generation_config.json, so pass the card's sampling yourself, for example temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5 without thinking
Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens
Validate any JSON it returns, since structured output isn't documented

Qwen3-Coder-Next

Quickest start is ollama run qwen3-coder-next (Q4_K_M, 52 GB of files)
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
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
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
There's no thinking mode, so don't pass enable_thinking or wait for reasoning_content
Sampling per the card is temperature 1.0, top_p 0.95 and top_k 40
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
Validate any JSON it returns, since structured output isn't documented

Questions

Which is better for AI agents, Qwen3.5-4B or Qwen3-Coder-Next?

Qwen3.5-4B scores 61.2 (C) on agent readiness against Qwen3-Coder-Next's 57.6 (C), and leads in 2 of 5 scored categories. Qwen3-Coder-Next leads on agent interface and documentation and upkeep.

Other comparisons with Qwen3.5-4B or Qwen3-Coder-Next

Machine-readable

This page as Markdown /compare/qwen3-5-4b-vs-qwen3-coder-next.md · slim .min.md · JSON .json (or send Accept: text/markdown)
Each listing in full /api/v1/tools/qwen3-5-4b.json · /api/v1/tools/qwen3-coder-next.json
From a terminal anchor compare qwen3-5-4b qwen3-coder-next (the CLI)
Over MCP compare_tools {"a": "qwen3-5-4b", "b": "qwen3-coder-next"} at /mcp, no key

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