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

GLM-4.5-Air vs Qwen3-Coder-Next

GLM-4.5-Air scores 59.1 (C) on agent readiness against Qwen3-Coder-Next's 57.6 (C), and leads in 3 of 5 scored categories. Qwen3-Coder-Next leads on reach. 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

GLM-4.5-AirZ.ai (Zhipu AI) · Open-weight model C59.1/100#62 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

GLM-4.5-Air C

Best for Agents with two to four datacentre GPUs, or a provider account, who want a permissively licensed English and Chinese tool-calling model with wide runtime support.

Ahead on

Capability, 50 against 42
Risk, 65 against 50
Agent interface, 85 against 75

Watch for

Z.ai's smallest build is FP8 at about 135 GB in use, with no 4-bit build of its own

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

Reach, 85 against 65

Watch for

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

Score by category

Category	Weight this run	GLM-4.5-Air	Qwen3-Coder-Next	Edge
Capability	35%35	50	42	GLM-4.5-Air +8
Reach	25%25	65	85	Qwen3-Coder-Next +20
Risk	20%20	65	50	GLM-4.5-Air +15
Agent interface	10%10	85	75	GLM-4.5-Air +10
Documentation and upkeep	10%10	38	41	Qwen3-Coder-Next +3
Negative events	≤15	0	0
Total		C 59.1/100	C 57.6/100

Facts side by side

Fact	GLM-4.5-Air	Qwen3-Coder-Next
Kind	Open-weight model	Open-weight model
Vendor	Z.ai (Zhipu AI)	Alibaba (Qwen team)
Hosted endpoint	no (local only)	no (local only)
Transports
Auth	None	None
Pricing	Free	Free
x402	no	no
Licence	MIT	Apache-2.0
Read-only variant documented	no	no
llms.txt	no	no
Last release	2025-08-11	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

GLM-4.5-Air

An English and Chinese cousin for agent, coding and tool-calling work, with switchable thinking and wide runtime support. It needs about 135 GB for Z.ai's FP8 build, two to four H100s by Z.ai's figures, and Z.ai and Novita host it. The main risk is age, with files from August 2025 and no answers from Z.ai since.

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

GLM-4.5-Air

Z.ai's FP8 recipe is python3 -m sglang.launch_server --model-path zai-org/GLM-4.5-Air-FP8 --tp-size 4 --tool-call-parser glm45 --reasoning-parser glm45, and 2 H100s are enough below the full 128K context
Use the glm45 tool and reasoning parsers for this model, the glm47 parser in Z.ai's current README is for GLM-4.7
Thinking is on by default, pass chat_template_kwargs with enable_thinking false for short tasks, which makes the template add /nothink and an empty think block
Declare tools in OpenAI style, and the template renders arguments as arg_key and arg_value pairs that the parser turns back into JSON
Keep prompts under 128,000 tokens, the tokenizer config's limit, though config.json allows 131,072
Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented for the weights

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, GLM-4.5-Air or Qwen3-Coder-Next?

GLM-4.5-Air scores 59.1 (C) on agent readiness against Qwen3-Coder-Next's 57.6 (C), and leads in 3 of 5 scored categories. Qwen3-Coder-Next leads on reach.

Other comparisons with GLM-4.5-Air or Qwen3-Coder-Next

Machine-readable

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

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